Systems and methods for rational selection of context sequences and sequence templates

ABSTRACT

Provided are systems and methods for rational selection of context sequences and sequence templates including a computer implemented method for obtaining a repository of attributes sets where the attributes sets are statistically associated with a sequence template representing two or more context sequences.

FIELD OF INVENTION

The present invention relates to the analysis of polynucleotide sequence clusters, and in particular for the characterization of such sequence according to one or more parameters.

BACKGROUND OF THE INVENTION I. Analyzing Polynucleotide Sequences by Clustering

The increasing amounts of polynucleotide sequence data present an analytical challenge. Such large amounts of data on the one hand provide an opportunity for extensive research, but on the other hand are difficult to analyze by conventional analytical methods. However, one method that has been found to be generally effective for analyzing such large amounts of sequence data is clustering.

Clustering may be performed in a variety of methods. Hierarchical clustering, for example, seeks to create by steps of either mergers or divisions, a hierarchy of segments or clusters. Agglomerative approaches build the hierarchy of clusters by steps of such mergers. Some approaches combine the above two¹.

In addition, there are also non-hierarchical methods, which do not seek to create a hierarchy of segments or clusters. The K-Means clustering algorithm is an example of such a clustering technique. It has been used in combination with other techniques, for example, for exploring protein structure². It was also used to identify recurring local sequence motifs for proteins³.

II. Context Polynucleotide Sequence Analysis

Heidecker and Messing⁴ found the NNANNAUGGC motif in the AUG context. Joshi⁵ identified the consensus sequence of AAAAACAA[A/C]AAUGGC.

More recently, a survey which included 5074 plant genes demonstrated that higher plants have an AC-rich consensus sequence, aaaaacaA(A/C)aAUGGCg as a context of AUG⁶. These finding were recently supported.

Analysis of 5′ untranslated region of mRNA of vertebrates were initially focused on conserved consensus sequence signals which accommodated translation initiation⁸. Studies which followed, attempted to analyze the consensus sequence about said translation initiation signal⁹. The later study has demonstrated conserved purines at position −3 and at position +4. The following conserved sequences were identified in the same study: (GCC)GCC(A/G)CCAUGG.

Consensus sequences are useful in research for locating the translation initiator codon. The untranslated leader sequence may additionally influence gene expression levels¹⁰. It was previously appreciated that Kozak-Like elements in the context of the initiator codon indeed affect expression levels^(11,12,13,14). Therefore, in U.S. Pat. No. 7,253,342, leader sequence was used to directly influence the expression of the specifically attached gene by either increasing expression, or for maintaining stable mRNA levels¹⁵.

SUMMARY OF THE INVENTION

In one aspect, the present invention is directed to a computer implemented method for obtaining a repository of attributes sets, wherein attributes sets are statistically associated with a sequence template representing two or more context sequences, comprising:

(a) obtaining a dataset of context sequences;

(b) transforming each context sequence to a sequence template, thereby obtaining a dataset of sequence templates;

(c) clustering said dataset of sequence templates into a plurality of clusters according to a distance formula; wherein at least one of said clusters is statistically associated with at least one attributes set;

(d) inserting into said repository each of said clusters and said attributes set which is statistically associated with said each of said clusters;

In one embodiment, the dataset of context sequences of step is further subjected to multiple sequence alignment. The later provides a solution in a particular instance, for example, where the context sequences in the data set are of different lengths or where the context sequences in the data were substantially affected by insertion/deletion regions.

In another aspect, the present invention is directed to repository obtained by the computer implemented method obtaining a repository of attributes sets as defined.

In a second aspect, the present invention is directed to a computer implemented method for identifying a sequence template as statistically associated with an attributes set of interest, comprising:

(a) providing a repository of attributes sets; wherein attributes sets are statistically associated with a sequence template representing two or more context sequences;

(b) selecting an attributes set; and

(c) retrieving at least one sequence template statistically associated with said attributes set.

Optionally, the computer implemented for identifying a sequence template as statistically associated with an attributes set of interest, further comprises the step of merging at least two retrieved sequence templates.

In one embodiment, the attributes are selected from: the Gene Ontology Project (GO), Interpro annotation (European Molecular Biology Laboratory, EMBL), SMART (a Simple Modular Architecture Research Tool, found at http://smart.embl.de/), UniProt Knowledgebase (SwissProt), OMIM (by NCBI) PROSITE (by the Swiss Institute of Bioinformatics), Protein Information Resource (PIR), GeneCards, and Kyoto Encyclopedia of Genes and Genomes (KEGG).

In a third aspect, the present invention is directed to a computer memory system comprising a plurality of tree topologies representing plurality of (k) heaps, wherein the plurality of tree topologies is managed through a common interface; and (k≧1).

In a one embodiment, the heaps are min heaps. In another embodiment, the heaps are max heaps.

In yet another embodiment, an active subset of heaps is held in Random Access Memory (RAM), while the rest of said heaps are maintained on a secondary storage.

In yet another aspect, the invention is directed to a computer implemented method for clustering a plurality of polynucleotide sequences, comprising: determining an attributes set for the plurality of polynucleotide sequences; and clustering the polynucleotide sequences into a plurality of clusters according to values of said attributes set.

In another aspect, the invention is further directed to a method of preparing a polynucleotide construct, comprising:

(a) identifying a sequence template as statistically associated with an attributes set of interest according to the method of the present invention;

(b) preparing a polynucleotide construct having at least one portion operably linked to a context sequence; wherein said context sequence is characterized as having either 80%-85%, 85%-90%, or 90%-100% homology with said sequence template.

In another embodiment, the preparing step comprises synthesizing said context sequence. In another embodiment, the preparing step comprises the preparing of an expression vector comprising said context sequence. In another embodiment, the preparing step comprises the preparing of a probe comprising said context sequence.

In another aspect, the present invention is directed to a computerized system configured for identifying a sequence template as statistically associated with an attributes set of interest, the computerized system comprising: context sequence clustering module, configured to cluster said sequences into a plurality of clusters; an enrichment analysis module, configured to provide enrichment appraisal, wherein context sequence clustering module being communicatively coupled to the enrichment analysis module.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The materials, methods, and examples provided herein are illustrative only and not intended to be limiting.

Although the present invention is described with regard to a “computer” which may optionally be implemented on a “computer network”, it should be noted that optionally any device featuring a data processor and/or the ability to execute one or more instructions may be described as a computer, including but not limited to a PC (personal computer), or a server. Any two or more of such devices in communication with each other, and/or any computer in communication with any other computer may optionally comprise a “computer network”.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which:

FIG. 1 illustrates, in accordance with one embodiment of the present invention, an exemplary computerized system on which the present invention may be implemented.

FIG. 2 a illustrates, in accordance with one embodiment of the present invention, an exemplary user interface for obtaining a requested function array from a user.

FIG. 2 b illustrates, in accordance with one embodiment of the present invention, an exemplary user interface for obtaining a function array or attributes set of interest which is optionally provided by a user.

FIG. 3 illustrates, in accordance with one embodiment of the present invention, an exemplary user interface for proposing the predicted context sequences for synthesis.

FIG. 4 illustrates, in accordance with one embodiment of the present invention, an exemplary viewer application reproducing a context sequence, the cellular function annotations and the size of the context sequence cluster.

FIG. 5 illustrates, in accordance with one embodiment of the present invention, an exemplary data structure of a function attribute array or the cellular function annotations array.

FIG. 6 illustrates, in accordance with one embodiment of the present invention, a simplified example of ascertaining the processing order of templates. (a) and (b) are two clusters of templates having equal minimum distances to a common template.

FIG. 7 illustrates, in accordance with one embodiment of the present invention, a Simplified example of ascertaining the processing order of clusters. (a′) is a new cluster representing a merger of closest neighbors of (a) which was shown in FIG. 6, and (b) is to be handled subsequently.

FIG. 8 illustrates, in accordance with one embodiment of the present invention, a multiple-tree-array topology within a memory module. The top item is defined as the element having the minimal key value amongst the (k) specific min heaps as shown.

FIG. 8 a illustrates, in accordance with one embodiment of the present invention, a multiple-tree-array topology within a memory module. The top item is defined as the element having the maximal key value amongst the (k) specific heaps as shown.

DETAILED DESCRIPTION OF THE INVENTION

The present invention, in some embodiments, is of a system and method for analyzing a plurality of nucleotide or other sequences. In other embodiments, the present invention relates to a system and method which provide more efficient memory structures and computational processes. The later system and method may optionally be used with the former embodiments or may optionally be used independently.

For the sake of clarity only and without any intention of being limiting, the below description is divided into three sections. Section I relates to the system of the present invention; Section II relates to embodiment for obtaining of a repository of attributes sets, statistically associated with context sequences and/or a sequence template representing the them; Section III relates to embodiments which provide more efficient memory structures and computational processes; Section IV details embodiments of a computer implemented method for identifying a sequence template as statistically associated with an attributes set of interest; and Section V relates to experimental examples using such embodiments;

Nomenclature

For the purposes of the present invention, “cellular function annotation”, “function attribute”, and “attribute” of a given gene shall mean an attribute, term, characterization, molecular function annotation, or biological process annotation describing a gene or a gene product. The terms can be used interchangeably and synonymously herein. The cellular function annotation are typically reported in variety of sources such as, but not limited to, the Gene Ontology Project (GO), Interpro annotation (European Molecular Biology Laboratory, EMBL), SMART (a Simple Modular Architecture Research Tool, found at http://smart.embl.de/), UniProt Knowledgebase (SwissProt), OMIM (by NCBI) PROSITE (by the Swiss Institute of Bioinformatics), Protein Information Resource (PIR), GeneCards, Kyoto Encyclopedia of Genes and Genomes (KEGG). It should be emphasized that the above terms and attributes are continuously updated, and new versions are made available on a monthly basis and therefore the systems and methods of the present invention should be interpreted as limited by the gene annotation known at time of filing the application for the invention. Furthermore, it should be noted that the user may optionally operate the method or system of the present invention with any such function attributes, as long as they may be characterized according to a numerical grade; such a grade may optionally be Boolean (“1” or “0”), or alternatively may feature a plurality of discrete numbers or continuous numerical values.

By way of an illustrative example, the term or attribute “cell adhesion” associated to Homo sapiens discoidin domain receptor tyrosine kinase 1 (DDR1, RefSeq accession: NM_(—)001954) is a cellular function annotation. This attribute is found, for example, in

Gene Ontology under GO:0007155.

The term “complete function attributes set”, and “complete attributes set” shall mean the complete set of function attributes i.e. all function attributes stored in a repository of the present invention. The terms can be used interchangeably and synonymously herein.

The term “function attributes set”, “attributes set” and “function attributes array” shall mean a subset of the complete function attributes set. The terms can be used interchangeably and synonymously herein. Optionally, the function attributes array can be used to represent a specific user selection in which the user manifests particular function attributes of interest. The user can typically select an attributes set in order to perform the computer implemented method of the present invention for identifying a sequence template whish is statistically associated with the attributes set of interest.

Alternatively, by way of non-limiting example, the attributes set can be used to represent attributes set which is statistically associated with a sequence template. The later can be identified in functional appraisal performed by the methods and system of the present invention. The later is typically performed with respect to a cluster of context sequences or attributes associated with a gene operably linked to the context sequences of the cluster. The results of the functional appraisal performed can thus be represented by an attributes set.

The attributes set optionally feature an array of real numbers, with each of said numbers representing a level of association of a particular annotation or attribute. It can also feature an array of binary digits, where each of said binary digit representing association with a particular annotation or attribute. In this case, ‘0’ can represent the absence of association of a particular function attribute and ‘1’ can indicates statistical association of the particular function attribute.

The term “sequence” shall mean a polynucleotide sequence, continuous or otherwise, of nucleotides being selected from a group consisting of deoxyribonucleotides (DNA) and ribonucleotides (RNA), genomic or otherwise, coding or non-coding. Sequence does not encompass therefore gene order in general or genomic meta structures.

The term “context sequence” shall mean a sequence which regulate or affect a gene product (mRNA, polypeptide and alike). Context sequences consist of at least portion of un-translated sequence. By way of non-limiting example, a context sequence may comprise a sequence which is operably linked to a coding region, sequence affecting expression level of a gene product or otherwise a sequence regulating gene product (or activity). Therefore, a context sequence may comprise a stretch of nucleotides preceding the translation initiation codon of mRNA molecule. A context sequence may comprise a stretch of nucleotides downstream to the translation termination codon of mRNA molecule. In the above examples the context sequence was defined by its relative location to a coding region. However, a context sequence of the present invention may further comprise a promoter, enhancer, inhibitor or other regulatory region.

For the purposes of the present invention, “template” or “sequence template” shall include a matrix T_(4×l), where (l) denotes the length of the context sequences or aligned context sequence which are represented by the template. The template can either represent the distribution of each nucleotide for each position along a context sequence. The template can further include a matrix (T) where T[a,i] holds the distribution of nucleotide (a) at position (i) in of the context sequences represented. The terms can be used interchangeably and synonymously herein.

As discussed below, at initiation of a clustering method each context sequence is transformed to a template. The skilled person in the art would appreciate that context sequence transformation into a template can typically be performed as an integral part of matrix allocation. By way of a non-limiting example, if a single context sequence has ‘A’ at position 3 then T[‘A’,3]=1.0 (T[‘C’,3]=0, for obvious reasons).

For example, the sequence ‘AG’ is represented by a template having the following distribution matrix:

Simplified example of distribution matrix held in a template, at initialization. At this stage, the template represents a single sequence having the prescribed distributions.

Position = 0 Position = 1 P(A) 1.0 0 P(G) 0 1.0 P(T) 0 0 P(C) 0 0

During the prosecution of the methods of the present invention a sequence template can represent a cluster of context sequences and the distribution matrix will thus reflect the distribution of nucleotides which characterizes the context sequences within the cluster. The sequence template can typically further comprise a set of gene names or unique identifiers which are operably linked or affected by the context sequences represented thereby.

The term “repository” and “database” shall mean a database or any system configured for insertion and retrieval of information of the present invention. The terms can be used interchangeably and synonymously herein.

The repository of the present invention is typically configured for insertion and retrieval of attributes, attributes set, and context sequences. The later are typically in a form of sequence of ASCII characters. The repository of the present invention can also be configured for insertion and retrieval of sequence templates which can typically comprise an array of numbers, or a 2D matrix of numbers. Moreover, a repository of the present invention is typically configured to insert and retrieve pointers or association between information elements stored therein. In particular, the repository of the present invention can insert and retrieve an attributes set, a sequence template, and to associate between them; so as to enable retrieval of a sequence template together with at least one respective attributes set. Moreover, it can be configured to enable retrieval of an attributes set together with at least one respective sequence template.

The term “multiple sequence alignment”, “MSA” or “alignment” shall have the ordinary meaning as used by the skill person in the art of bioinformatics. CLUSTAL W is typical software package used for that purpose, and can be utilized by usage of default values and other values being adapted for the particular dataset in hand.

The term “synthetic context sequence” or “predicted context sequence” shall mean at least one context sequence or sequence template representing said context sequence that was identified by the systems and methods of the present invention, as statistically associated with an attributes set of interest.

Embodiments of the invention can be used in a general purpose computer system suitably adapted and designed for performing the extensive context sequences clustering, enrichment analysis and comparison.

Section I

FIG. 1 illustrates, in accordance with one embodiment of the present invention, an exemplary system on which the present invention may be implemented. In an embodiment, the computerized system 100 permits clients or users to provide an attributes set of interest for analysis 135. Typically, the attributes set can consist of two or more attributes of interest.

The clients or users can further provide a dataset of context sequences 105 as input information; thereby obtaining a dataset of context sequences for analysis. The context sequences can typically further comprise a set of gene names or unique identifiers which are operably linked or affected by the context sequences, respectively. The attributes set of interest 135 and context sequences 105 can be entered via a user interface specifically configured for that purpose. Where the system 100 is implemented on a computer network, the attributes set 135 and context sequences 105 can be provided through a browser application, such as, but not limited to web browsing application. Alternatively, the attributes set 135 or the context sequences 105 can be comprised in a file. The file can be uploaded to the system 100 though either a network or other information uploading methods known in the art for that purpose.

The context sequence clustering module 110 clusters the context sequences as described hereinafter. Typically, the dataset of context sequences 105 comprises a huge amount of sequence information. In turn, each context sequence is transformed into a sequence template. Clustering of the dataset of sequence templates is performed and results with plurality of clusters. In turn, each gene cluster or the genes which are regulated or affected by the context sequences within the cluster, is subjected to functional appraisal. The result of the functional appraisals is a plurality of clusters each statistically associated with their respective attributes set. The system and method of the present invention enables obtaining of heterogeneous clusters, as defined below.

The clustering procedures of the present invention are, inter alia, utilized in order to obtain a repository of attributes sets, statistically associated with a sequence template. Optionally, the sequence template represents two of more context sequences. The later may not be identical. Therefore, the clustering procedures of the present invention enable obtaining a heterogeneous repository, as defined hereinafter.

The clustering procedures of the present invention can use a 2-dimentional distance matrix to store and retrieve distance related information. However, in order to produce improved performance, distance related information is typically stored and retrieved from computer memory system comprising of plurality of heaps 130, or heap data structures. Data items which are stored and retrieved in the computer memory system 130 of the present invention typically comprise references pointing at two matrixes or templates and a real number. Each said templates represent a cluster of context sequences and the real number measures the distance between the clusters. Optionally, data items may further comprise information such as, but not limited to, gene names or unique identifiers of genes which were classified within the clusters. Alternatively, a template can further comprise information such as gene names or unique identifiers genes which were classified within the cluster which is represented by the template.

Clustering of the present invention is typically performed by the clustering module 110. The context sequence clustering module 110 stores and retrieves data items from the computer memory system (or memory module) 130. The structure of the computer memory system is described below. In essence, the memory system is based on plurality of Heap data structure which was restructured and remodeled, as described below, to improve performance especially where large data set are in hand. For the purpose of the present application, the memory systems shall also be referred to as “multiple-tree-array” the particulars of which are described below. The later typically comprises min heaps and adheres to the invariant according to which the top data item in the multiple-tree-array is a data item referencing a pair of templates having a minimal distance between them. The multiple-tree-array allows the system 100 to perform the clustering of the context sequences and enrichment analysis at an extremely efficient manner reducing the complexity by about one order in comparison to typical 2-dimentional distance matrixes.

The enrichment analysis module 120 performs enrichment appraisals or functional appraisals as described below. In an embodiment, the context sequence clustering module 110 sends a request to the enrichment analysis module 120. The request comprises a data set of context sequences or unique identifiers representing the context sequences within a cluster or unique identifiers of genes regulated or otherwise affected by context sequences. The request typically channeled through either a communication port, BUS or a computer network 115 to the enrichment analysis module 120.

In an embodiment, clusters of context sequences together with their respective enrichment appraisals can be stored in or retrieved from a repository or database 125. In an embodiment, the results of enrichment appraisals are represented by an attributes set or function attribute array being associated with respective cluster or clusters.

The function array comparator 140 is adapted to compare the attributes set of interest (typically provided by a user), with said stored enrichment appraisals retrieved from the repository 125.

FIG. 2 a and FIG. 2 b illustrate, in accordance with one embodiment of the present invention, an exemplary user interface 200 for obtaining an attributes set of interest from a user. As an alternative, an attributes set is obtained from a client over the network (not shown). The client may be local or remote, either human or automated procedure performed on a computer system. Typically, the user select an attributes set from a list of function attributes 210. The list of function attributes contains at least a sub set of a complete function attributes set. In one embodiment, the user selects the function attributes of interest in order retrieve a sequence template statistically associated with his selection.

The sequence template retrieved can be used in order to design a context sequence for the purpose of either synthesis or manufacture of polynucleotide construct, or vector. In a one embodiment, the context sequence designed comprises the most dominant nucleotide in each position along the sequence template retrieved. In another embodiment, the context sequence designed comprises 80%-85%, 85%-90%, or 90%-100% homology with sequence template or the sequence comprising the most dominant nucleotide in each position along the sequence template.

The subset of function attributes selected by the user can be represented by a function attribute array or the attributes set. The manual selection can be performed with checkboxes 215 which indicate whether a particular function attribute was selected. As the complete function attributes set stored in the computerized system 100 may exceed the page size, page scroller 205 can provide means for navigating through the entire list of function attribute. The list of function attributes can be organized by several techniques, such as but not limited to, lexicographical order, classification, or source of the function attribute. In another embodiment, the user interface includes textboxes 220 in which a user enters the importance degree or confidence level associate with a particular function attribute.

The system of the present invention is adapted to retrieve an enrichment appraisal previously stored in the repository. The enrichment appraisal typically shares a similarity with an attributes set of interest. In another embodiment, the system of the present invention is adapted to retrieve an enrichment appraisal which shares similarity with an attributes set of interest at a predetermined threshold. Alternatively, the system of the present invention is adapted to retrieve an enrichment appraisal which shares maximal similarity with an attributes set of interest.

The output 150 comprises a cluster of context sequences or a sequence template representation thereof, which is statistically associated with said retrieved enrichment appraisal(s). In an embodiment, the output 150 comprises a cluster of context sequences or sequence template representation thereof which are statistically associated with said retrieved enrichment appraisal(s).

Those skilled in the art would appreciate that the invention may be practiced with other computer based system configurations, including network PCs, or hardware specifically designed to perform the procedures and functionalities contemplated hereinafter. The invention may also be practiced in distributed computing environments where procedure of the present invention is performed by remote dedicated processing devices that are linked through a communications network. In a distributed computing environment, for example, program modules such as 110, 120, 125, 130, and 140 may be located in both local and remote apparatus.

The components shown in FIG. 1 are only examples, and are not intended to suggest any limitation as to the scope of the functionality of the invention; the invention is not necessarily dependent on the features shown in FIG. 1.

FIG. 3 illustrates, in accordance with one embodiment of the present invention, an exemplary user interface 300 providing an identified context sequence 310 or a sequence template representing a cluster of context sequences statistically associated with the attributes set of interest. Typically, the identified context sequence or the sequence template consists of those which are statistically associated with stored attributes sets sharing maximal similarity with the attributes set of interest. Similarity or similarity degree is determined by the method described below.

In an embodiment, the user interface 300 includes a textbox, label, or information box 320. Each context sequence 310 or sequence template (not shown) can be associated with textbox, label, or information box 320. The textbox, label, or information box may include statistical confidence level of the context sequence such as p_value or a false discovery rate (FDR) or other enrichment estimator. The page scroller 305 can provide means for navigating through the entire list of context sequences where, for example, the predicted context sequences exceed the window size of the user interface 300.

FIG. 4 illustrates, in accordance with one embodiment of the present invention, another exemplary user interface 400 consists of a sequence template 410 representing a cluster of context sequences, said statistically associated attributes set 420 and the size of the cluster 430. Typically, the user interface 400 can be reached be double clicking on a predicted context sequences 310. The distribution table 415 can comprises a matrix representing the probability of a given nucleotide at a particular position along the context sequences of the current cluster viewed. Each column can represent a position along a predicted context sequence. The most dominant nucleotide at a particular position along the identified context sequences can appear at the top of the respective column 410. Where two nucleotides share similar of identical dominance level both can appear at the top of the respective column 425.

In another embodiment, the user interface 400 is utilized for viewing the clustered context sequences 410 comprising polypeptide sequences. In such an embodiment, the distribution table 415 can comprise a matrix representing the probability of a given amino acid at a particular position along the predicted context sequence. Each column can represent a position along a predicted context sequence. The most dominant amino acid at a particular position along the predicted context sequence can appear at the top of the respective column 410, while two amino acids sharing similar or identical dominance levels both can appear at the top of the respective column 425.

FIG. 5 illustrates, in accordance with one embodiment of the present invention, an exemplary data structure of a function attribute array or the attributes set 500. The attributes set 500 typically features a matrix of cells or items 510. Each of the cells in the matrix can comprise several fields or objects. In an embodiment, the first field of object is a function name/attribute 520 and the second is a value 530 associated therewith. The value 530 may optionally represent a Boolean variable. In an embodiment, where a Boolean variable in a cell holds #true, for example 530, the attributes set includes the particular attribute 520. On the other hand, where a Boolean variable in a cell holds #false the attributes set does not include the particular attribute. In another embodiment, value 530 can be represented a Real variable which represents the statistical confidence level of the particular attribute. By way of non-limiting example, where value 530 hold “1.0E-17”, the function attribute array highly likely to include a particular attribute 520. On the other hand, if value 530 hold “1.0”, the function attribute array most likely does not include the particular attribute 520.

One of ordinary skill in the art would appreciate that the data structure of the attributes set can be varied almost indefinitely. Many other data structures can be employed for storing a subset of attribute. By way of non limiting examples, the attributes set may optionally be stored as a Dictionary or hash table. Other one limiting examples: array of pair <string, boolean>, or indeed a 2D matrix where one dimension is the function attribute and the other dimension is a value.

The attributes set of interest 135 can be represented by the function attribute array 500.

As a mere illustration of the functionality of the present invention, the user may seek to identify one or more sequence templates associated with an attributes set of interest. Assume that the user wishes to consider immunoglobulin and transcription regulation with respect to humans. The user selection of interest is transformed into an attributes set 135 which are typically represented by the function attribute array 500. The function array comparator 140 compares the attributes set received comprising the user selection with said stored enrichment appraisals. The later are retrieved from the repository 125, with respect to humans. Typically, the user can request retrieval of a stored sequence template which is statistically associated with the specific function attributes chosen by the user. Alternatively, the user can retrieve the context sequences which were clustered together, and represented by the template. The user may find it advantageous to design or synthesize polynucleotide or polypeptide sequences on the basis of their functional association.

Therefore, the system and methods of the present invention can thus be used in preparing a polynucleotide construct, comprising: identifying a sequence template as statistically associated with an attributes set of interest by a user or client; and preparing a polynucleotide construct having at least one portion operably linked to a context sequence; wherein said context sequence is characterized as having either 80%-85%, 85%-90%, or 90%-100% homology with said sequence template. The user may wish to synthesize said context sequence, by utilizing any synthesis method known in the art for that purpose. Alternatively, the user may construct an expression vector comprising said context sequence or prepare a probe comprising the identified context sequence.

Homology in the range of X %-Y % shall be defined as identity score in the percentage range of X %-Y %. Said identity score is typically provided by an alignment analysis program. The alignment analysis can be performed using a numerous commercial sequence analysis packages, such as, but not limited to WATER (Smith-Waterman local alignment) provided by EMBOSS (European Molecular Biology Open Software Suite) operated with either default values or open gap penalty: 11, extended gap penalty: 0.5, and the default EDNAFULL or BLOSUM62 similarity matrix. Therefore, Homology in the range of %80-%100, as an example, shall mean that an identity score which ranges between 80%-100% using WATER according to the parameters set above.

Without limiting the applications of the presently described, the system described above is further adapted to execute the methods described hereinafter. In particular, the method for obtaining a repository of attributes sets, wherein attributes sets are statistically associated with a sequence template representing two or more context sequences, and the method for identifying a sequence template as statistically associated with an attributes set of interest.

Section II

The K-Means algorithm and its derivatives require the initial input of k-criterion from the user. For some clustering purposes, as the present purposes, the initial input of k-criterion is simply not known. For example, the user might not know how many clusters (k) will achieve well separated clusters enriched with functional attributes. It may well be any of 1≧k≧N possibilities; for example where N=16,000 there are 16,000 possibilities.

Furthermore, the results of the K-Means algorithm are extremely sensitive to the initial random selection of cluster representatives. It was recently demonstrated that the worst-case running time of K-Means is super-polynomial i.e. 2^(Ω(√{square root over (n)})16).

Therefore in one of its embodiments, the present invention utilizes a different computer implemented method (hereinafter: “LBDL (Lower Bound Distance Limit) clustering method”). The LBDL is preferably used for large datasets e.g. N>16000 context sequences, and/or where no prior information relating to the suitable number of clusters is available i.e. k is unknown. While LBDL is preferred over K-means for example, the present invention is not limited to a particular clustering algorithm and may in fact optionally be implemented with any type of clustering.

Accordingly, the LBDL clustering method of the present invention does not require k-criterion at all. Instead, it requires a lower bound distance limit (LBDL) between clusters, as detailed below. This lower bound criterion is advantageous because it encapsulates actual practical meaning to the person skilled in the art i.e. distance between clusters of nucleic or peptide sequences.

The LBDL Clustering Method and Implementation Considerations

For the purposes of the present invention, “lower bound distance limit” shall mean a predetermined real number representing the lower bound distance limit.

For the purposes of the present invention, “lower bound distance limit invariant” shall mean the following invariant (hereafter: the LDBL-invariant): during the execution of the computer implemented LBDL clustering method, clusters will not merge where the distance between them is greater than a given distance limit.

In the present invention “data item”, “heap item”, or “(i, j, d(i, j))” shall mean a data item in a memory structure comprising (i) representing a first template, (j) representing a second template, and d(i,j) the distance between the templates. One of ordinary skill in the art would appreciate that the data item can be presented be other means such as, but not limited to, other data items, or differently ordered data items, all which essentially hold the template information and distance information relating thereto.

The LBDL method is provided hereinafter. In essence, each sequence under analysis is transformed to an information node or, as exemplified below, a sequence template. The algorithm efficiently performs merger operations, until satisfaction of the LBDL criteria. Each unraveled cluster is in turn subjected statistical functional appraisal. Each sequence template representing a cluster of context sequences is stored together with the associated results of the functional appraisal in a repository:

For the purposes of the present application VP shall mean a comment or remark.

-   -   1. for each context sequence in dataset allocate a         template//representing the distribution of nucleotides along the         sequence. This step is an initialization step in which each         context sequence respectively represented by a template. A         particular embodiment or template representation is detailed         below.     -   2. for each pair (i,j), i≠j, i, jε{Context-sequences-in-dataset}     -   3. insert (i, j,d(i, j)) into a multiple-tree-array.     -   // In steps 2-3 the distance between each pair of templates is         measured. Each pair of templates is then inserted into a         multiple-tree-array together with the distance between them. For         clarification (a,b,c) of step 3 represents an abstract data         structure or data item typically comprising 3 numbers, two of         which are identifying a pair of templates and the third is a         distance measurement between them.     -   4. prevMin=−1: List CurMin=Empty List;     -   // initialization of variables     -   5. While (! multiple-tree-array.empty( )) {// This is the main         loop     -   6. min=multiple-tree-array.DeleteMin( );     -   // current minimal data item stored in ‘min’. Retrieval of the         minimal data item is typically performed by executing DeleteMin(         ) procedure on a multiple-tree-array data structure.         Multiple-tree-arrays are defined below and by definition the         minimal data item is an item having minimum distance held         therein i.e. the data item represents a pair of templates         sharing the highest similarity.     -   7. CurMin.Insert(min);     -   // Insert the minimal data item stored in the         multiple-tree-array into CurMin (the List data structure defined         in step 4).     -   8. if (min.distance>lower_bound_distance_limit) Break;}     -   // The main loop continues until the lower_bound_distance_limit         criterion is satisfied. The Lower Bound Distance limit is         defined below. ‘Break’ shall mean end loop i.e. continue to step         12.     -   9. if (min.distance !=prevMin) {     -   // ‘!=’ means not equal. Therefore, where the condition of step         9 is satisfied, the previously handled distance stored in         ‘prevMin’ is not equal the distance of current minimum distance         i.e. ‘min.distance’. CurMin holds all items which were retrieved         from the multiple-tree-array and are having same distance. This         is done to ensure concurrent and equal treatment of data items         which share the same distance.     -   10. HandleCurrentTemplates(CurMin);     -   // As previously noted, CurMin stores all items which were         retrieved from the multiple-tree-array and are having same         distance. HandleCurrentTemplates( ) is a procedure which is         defined below, and in essence this procedure which handles the         merger operation(s) of the currently handled cluster(s).     -   11. prevMin=min.distance; CurMin.Empty( );}     -   // As all items of CurMin were handled by         HandleCurrentTemplates, initialization of the variables is         required to verify that CurMin is empty. ‘Empty( )’ is typically         a procedure which empties the CurMin List.         ‘prevMin=min.distance’ updates the “previous” minimal distance         with the current one (in turn the “current” is the “previous” in         the next steps).     -   12. Subject each remaining template to a functional appraisal.     -   // each cluster of context sequences which is represented by a         sequence template are subjected to a functional appraisal. This         is typically performed by first retrieving the names or unique         identifiers of genes regulated or affected by the context         sequence within a cluster; and secondly, executing functional         appraisal on the names or unique identifiers retrieved.     -   13. Store <Cluster, functional appraisal results>in a         repository;}}

In an embodiment, each sequence template(s) or context sequence(s) clusters are stored in a repository together with the associated functional appraisal result. The functional appraisal result can be stored or represented as an attributes set or a list. In an embodiment, the associated functional appraisal is represented by the function attributes array 500. The method therefore obtains a repository of attributes sets, where the attributes set is statistically associated with a sequence template or cluster of context sequences represented thereby.

Typically, a sequence template represents a cluster of two or more context sequences. The later may be either identical context sequences or typically context sequence consisting of different sequences. Moreover, the attributes set associated with a cluster of context sequence(s) can consist of two or more attributes.

A given cluster may also be associated with a particular attribute even where at least one of the context sequence (or gene affected thereby) is not characterized by the attribute. In other words, a cluster may be deemed as statistically associated with an attribute by functional appraisal even where a specific context sequence within the cluster is not particularly characterized by that attribute.

Therefore, a cluster in the present invention may therefore be deemed as a heterogeneous cluster. For the purpose of the present invention “homogeneous cluster” shall mean a context sequence cluster (or sequence template representing said cluster) wherein all context sequences in the cluster are of identical sequence. Alternatively, the term homogeneous cluster shall encompass a context sequence cluster (or sequence template representing said cluster) wherein all genes/context sequences in the cluster are characterized by an attribute. A “heterogeneous cluster” shall mean a sequence context (or sequence template representing said cluster) which is not a homogeneous cluster. In other words, a cluster exhibiting either: (1) at least one pair of non identical context sequences, or (2) statistical association to an attribute wherein at least one gene/context sequence is not characterized by the attribute.

A “heterogeneous repository” shall refer to a repository comprising at least one heterogeneous cluster. Examples 1 to 4 exemplifies numerous heterogeneous clusters detailed in Tables 1 to 4.

In an embodiment, either steps step 12 or 13 further comprise the step of discarding those attributes where the functional appraisal resulted with P_value greater than 0.3, 0.2, 0.1, and preferably greater than 0.05. The person skilled in the art would appreciate that other P_values can be selected for a particular data set in hand.

The lower_bound_distance_limit (LBDL) can be set to various values depending on the distance formula used and the sought degree of separation between the clusters. Where the distance formula used is d(V,W) (defined below) and (l) denotes the length of the context sequences, the LBDL can range between 2%×(2l) to 5%×(2l), 5%×(2l) to 20%×(2l), or 20%×(2l) to 55%×(2l). The later is the most preferable as an initial configuration for analysis.

In a one embodiment, the dataset of context sequences is further subjected to multiple sequence alignment. The person skilled in the art would understand multiple sequence alignment can result in gap insertions which in turn may lengthen the length (l) of the context sequences.

By way of an illustration of LBDL invariant, observe the following aligned sequence population (N=2540):

I) 2000 sequences comprising: “AAAAA”

II) 500 sequences comprising: “GGGGG”

III) 30 sequences comprising: “TTTTT”

IV) 5 sequences comprising: “TATAT”

V) 5 sequences: “GTGTG”

With the knowledge that the size of the population is 2540, it is difficult to predict that k=5 (i.e. 5 clusters) will produce well separated clusters of gene sequences (why not try k=6, 7, . . . 1001 and so forth). This is, of course, an exemplary instance. In reality, it may well be that k=1001 will be produce reasonably separated clusters in the vector space.

If the user would enforce k=2 (i.e. two clusters), the K-Means algorithm will cluster the population as follows:

A) 2000 sequences having “AAAAA” as central representative.

B) 540 sequences having effectively “GGGGG” as central representative.

Groups III-V are therefore completely ignored. This might be an unacceptable result. The LBDL clustering method of the present invention avoids this problem. In this regard, ignoring clusters of genes simply because they are relatively small in size is inappropriate and unacceptable because even clusters consisting of even just a few genes may well have great value.

With respect to the above clustering example, assume that the invariant ensures that LBDL=ε>0, ε represent a real number having a positive and almost zero value. For that Lower Bound Distance Limit, the only possible sequence mergers occur among identical sequences i.e. where the distance is 0≦ε. Merging the identical sequences together will result in the original clusters I-V.

In order to perform the clustering of the context sequences, parameterization of each context sequence is required. For that end, at the initialization stage of the clustering method, each context sequence typically requires transformation into a corresponding sequence template.

Distance measurements (d) between any pair of templates V and W can be performed as follows:

-   -   d←0     -   for each i: 0 to 1-1         -   for each aε{A,T,G,C}         -   {d+=|V[a,i]−W[a,i]|²}

Wherein: (l) denotes the length of the context sequences or alternatively the length or the aligned context sequences.

One of ordinary skill in the art would appreciate that these different distance formulas can be used for the purposes of the present invention. By way of non-limiting example, the distance calculation procedure can be varied such that the fourth step would comprise d+=|V[a,i]−W[a,i]|. Alternatively, the distance calculation procedure can be varied such that the fourth step would comprise d+=|V[a,i]−W[a,i]|′,tεN.

Merger of a pair of context sequence clusters V′ and W′, which are respectively represented by sequence templates V and W, can be defined as, follows: The function Merge/Cluster creates and/or returns a sequence template T, representing the cluster T′ consisting of both the context sequences of V′. and W′ (i.e. T′=V′∪W′). The sequence template T would hold the following matrix T_(4×l) as follows:

-   -   for each i: 0 to 1-1         -   for each aε{A, T ,G, C} perform:

$\left\{ {{T\left\lbrack {a,p} \right\rbrack} = \frac{{{A} \cdot {V\left\lbrack {a,p} \right\rbrack}} + {{B} \cdot {W\left\lbrack {a,p} \right\rbrack}}}{C}} \right\}$

This merger procedure can be referred to as “merge”, or “merger”.

In an embodiment, the above merger procedures can handle a merger of more than two context sequence clusters by using sequential merger procedures. By way of non-limiting example, merger of 3 templates may typically require 2 merger operations. As an illustration, the first merger can take place with respect to templates 1 and 2, the product of which can be denoted as new template 12′. A second merger can merge the new template 12′ with template 3 thereby producing a single template 123′ representing all the context sequences which were previously represented by the separate templates 1, 2 and 3.

Memory Allocation of Sequence Templates

Sequence templates as defined above may be designed as a data structure or object. The sequence template essentially represents a subset of context sequences from the dataset i.e. a cluster. The sequence template would, therefore, hold distribution information of each nucleotide at each position in the cluster. The sequence template will typically hold the specific sequences which are grouped together in the cluster represented thereby. Optionally, a sequence template further holds gene name(s) or unique gene IDs which are regulated or otherwise affected by the context sequences within the respective cluster.

At initialization, each sequence in the dataset is transformed to a sequence template.

Order of Merging Operations

For the purposes of the present invention “handling current templates”, “HandleCurrentTemplate( )” and “HandleCurrentTemplates( )” shall have the following meaning. Before the algorithm handles the current templates, any pair of templates (or indeed the clusters represented thereby) having equal distances measured between them are preferably stored in CurMin List. The order of merger the clusters or templates representing them will take place according to the order-invariant as explained and exemplified below.

As described above, the order of merger operations according to the LBDL clustering method is dominated by the distance between the clusters. However, the context sequence dataset might include subsets of numerous clusters having equal or substantially equal distances. The initial order of these clusters or the order of the context sequence may affect to final results of the algorithm. Therefore, in an embodiment, the clustering method of the present invention aims at reducing the sensitivity of the algorithm to the initial order.

To that end, the cluster of context sequences which share equal are handled together without preferring arbitrarily any particular cluster. In particular, where pairs of clusters (or templates representing them) shares a common cluster and the distance between the pair clusters is equal, as illustrated in FIG. 6, they are handled together, as explained below. More formally: given the pair (i,j), and the pair (l,m), (i, j, l, and m are clusters), said pairs will thus be defined as sharing a common cluster if and only if i=l or i=m or j=l or j=m.

The following invariant will therefore apply (henceforth: the order-invariant): As any stage of the execution of a clustering method, the common template having maximum number of neighboring clusters will be the first to merge or be handle i.e. the largest “cluster” of clusters currently (held in CurMin List) will be merged first. Subsequently, the algorithm merges the rest of the currently handled templates according to the order-invariant.

FIG. 6 illustrates, in accordance with one embodiment of the present invention, a simplified example of ascertaining the processing order of templates. (a) and (b), for example, are two clusters of templates having equal distances to a common cluster.

Observe that common template (a) has 4 neighbors and while the central representative of cluster (b) has only 3 neighbors. Therefore, cluster (a) will be processed first. FIG. 7 illustrates the application of the order-invariant according to which cluster (a), previously shown in FIG. 6, was merged prior to handling of cluster (b). As a result, (a′) is a new cluster representing the merger, and (b) is to be handled subsequently according to the order-invariant.

Following the merger operation, the multiple-tree-array is typically updated with all new distances between the pre-existing cluster (or templates representing them) and the newly merged templates.

For each pair of templates (i,j) where the d(i,j)>LBDL, the newly created (i, j,d(i, j)) need not be stored in the multiple-tree-array and can be totally ignored as explained before.

Otherwise, new heap item (i,j,d(i,j)) is inserted into the multiple-tree-array, with a single proviso. Said insertion should takes place unless the distance d(ij) is lower than min.distance, defined above. In that case, the handling of data items which are held in. CurMin List is temporarily suspended and these data items are re-inserted to the multiple-tree-array.

For example, assume the following data items are held in multiple-tree-array, as follows: (1,5, 120), (5,3, 120), (4,6, 120), (7,4, 125), (1,7, 126), (8,2, 130). Only the first 3 data items will be currently retrieved. These data items share equal distances (120) which is the minimal in the dataset. At a given stage in the execution of the method, these three data items will be held in CurMin List and will be handled together. As the rest of the heap items encapsulate greater distances i.e. 125, 126, and 130, they will be processed later on.

In an embodiment, sorted dictionary data structure is utilized in order to provide fast identification of a common template having the maximal number of neighboring templates. By way of non-limiting example, assume the sorted histogram data structure has the follow data structure: <number of template appearances, sequence template reference>. In the above exemplification, the 3 retrieved data items will generate the following histogram in Sorted dictionary: <2,5>,<1,1>,<1,3>,<1,4>, and <1,6>. The neighbors of template 5 will merge first, under the order-invariant (template referenced as ‘5’; is the common template having maximum number of neighboring clusters).

Complexity Considerations

Utilization of the LDBL-invariant exhibited impressive complexity improvements:

(1) As LBDL based clustering method, by definition, does not require to merge clusters i, and j where the d(i,j)>LBDL, then (i,j,d(i,j)) need not be stored at all .i.e. because the cluster pair i and j will never be merged or clustered together. Reduction of memory usage is therefore apparent.

(2) At any stage of the execution, if DeleteMin( )procedure retrieves the global minimum which is greater (>) than the LBDL, it directly entails that the rest of the data items in the multiple-tree-array also exceed the LBDL. Therefore, the algorithm can be immediately terminated.

In another embodiment, the present invention utilizes a computer implemented method (hereinafter: “Vector Space clustering method”). Alternatively, the VS clustering method is used for performing clustering which is a variant of LBDL method shown above. This VS method is particularly useful where the length of the context sequences is in the range of 3-17 characters. The skilled person in the art would recognize that range is largely affected by computation time, which is associated with the length, and the computer system employed. Computer systems having high computation capabilities may process context sequences of greater length, including but not limited to the range of 10-15, or even 10-20 characters.

The VS clustering method:

1. for each c = (a₁a₂a₃...a₁),a_(i) ε {A,T,G,C},1 ≧ i ≧ l { // for each possible sequence in the vector space of length (1). 2. List Cluster = null; 3. for each i ε {Context - sequence - in - dataset} { 4.  double distance = d(c,i); // calculate the distance between c and i 5.  if (distance <= lower_bound_distance_limit) { // cluster together context sequences, if the distance between them fall within the lower_bound_distance_limit 6. Cluster.Insert(i);} 7. Subject Cluster to a functional appraisal; // each cluster of context sequences which is represented by a sequence template are subjected to a functional appraisal. This is typically performed by first retrieving the names or unique identifiers of genes regulated or affected by the context sequence within a cluster; and secondly, executing functional appraisal on the names or unique identifiers retrieved. 8. Store <Cluster, significant functional appraisal results> in repository;}}

In a one embodiment, step 1 is replaced with: “for a given sub set of each c=(a₁a₂a₃ . . . a₁),a_(i)ε{A,T,G,C},1≧i≧l;”. In this manner, the method is utilized for a particular subset of context sequences of interest. The latter embodiment can be used to loop through a subset of possible sequences instead of looping through the entire vector space of possible sequences. This may be advantageous for achieving more efficient execution time in cases, for example, that some sequences are known not to feature substantial sequence patterns or important functional characteristics.

The VS differs from the LBDL in several aspects. For example, each context sequence in LBDL is classified into a single cluster. On the other hand, VS may classify each context sequence is several clusters. In that respect CVS is a “softer” classifier which sometimes can be advantageous because a single context sequence may be associated with multiplicity of functional attributes or attributes set. Another difference lies in the fact that VS typically spans thorough the entire vector space of all possible sequences i.e. even sequences which are absent from the context sequences of the data set. This is especially advantageous where synthetic or predicted sequences cannot be found in vivo. This is exemplified in the Step 1, where the analysis is performed for each (c) representing a possible sequence (not necessarily a context sequence of the data set).

Section III

The present invention, in some embodiments, relates to an implementation of specialized memory structures and processes for computations. These structures and processes may optionally be implemented with the embodiments described above and/or may also optionally be used independently.

Memory module for holding Parameterized Information

Traditionally, the easiest and most straight forward approach to manage distance information of a dataset is a “distance matrix”. The later typically comprises 2D matrix of distances, such that each cell in said matrix holds the distance between a pair points of a set. A distance matrix is typically a symmetric N×N matrix containing real numbers as elements, given N points in a set. However, in large data sets, as might occur in the present case, the distance matrix performance is unacceptable. The performance time of retrieving the minimal or maximal element stored in the distance matrix is impractical for large data sets i.e. time for retrieving minimal/maximal element stored in the distance matrix.

Consider an exemplary size of the context sequences data set having N=16,000 i.e 16,000 context sequences. The matrix size would supposedly be O(N²) because the distance information represents all pair of said context sequences data set. Utilizing distance matrix would entail a typical retrieval time of a single minimal (or maximal) element in time complexity of O(N²). This renders the 2D distance matrix as unfavorable for use in the present invention, especially in case of large datasets.

In the present invention, “key” is a parameter within a data field comprising a value stored within a data item, or node. Preferably, key is a parameter capable of at least semi-order. By way of non-limiting example, a key may comprise a real number stored in a data item. Where (A) is data item, “KEY(A)” shall mean the parameter within a data field of data item (A). As an example, the key in a data item (i, j, d(i, j)) of the present invention can be the field consisting the distance between the pair of clusters i and j.

“heap” is a data structure based of tree topology that satisfies a general heap invariant as follows: For each pair of elements, items or child nodes in a heap, X and Y: where X is a child node of Y, then KEY(Y)≧KEY(X) i.e. The node having the maximum value as key (“greatest element”) is the top node (or root node) of the heap. This heap is typically referred to a max-heap. Where KEY(Y)≧KEY (X), the smallest element is always the top node, and the heap is referred to as a min heap. “DeleteMin( )” or “deletion” shall mean removing and retrieving the root node of a min-heap. “Insert( )” or “insertion” shall mean adding a new element to a min heap. Heap shall further mean as defined in Corman et al¹⁷ which is incorporated herein by reference.

A min heap provides an efficient data structure in which retrieving a minimal element is performed at O(log N). The latter is clearly more efficient in comparison to the traditional distance matrix at about 2 orders in magnitude.

For large data sets, however, a min heap is utterly inappropriate. To hold the complete distance dataset in a min heap is impractical. A PC having 1 GB available RAM and equipped with 3 GHz Intel Pentium processor can handle a min heap of about 300,000 data elements which means that data set cannot be greater than about N=600.

Therefore, in one of its aspects, the present invention provides a “multiple-tree-array” as defined and exemplified below. For the purpose of the present invention, “multiple-tree-array” shall mean memory module or data structure comprised therein employing plurality of tree topologies representing plurality of min-heaps, wherein the plurality of tree topology is managed through a common interface. There the present invention is directed to a computer memory system comprising a plurality of tree topologies representing plurality of (k) heaps, wherein the plurality of tree topologies is managed through a common interface; such that (k≧1).

FIG. 8 illustrates, in accordance with one embodiment of the present invention, a multiple-tree-array topology within a memory module. The top item, the item having the minimum distance, is the element having the minimal key value amongst the (k) min heaps as shown. Therefore, in one embodiment the computer memory system comprises min heaps.

The global minimum in the multiple-tree-array is defined as the minimal element (or minimal root element) amongst the min heaps comprising the multiple-tree-array. In other words, the minimal element is holding the minimal key value in comparison to all (k) min heaps which comprises the multiple-tree-array (hereafter: min-heap invariant). In one embodiment, the global minimum is the minimal distance between a pair of context sequences or sequence templates.

FIG. 8 a illustrates similarly, in accordance with another embodiment of the present invention, a multiple-tree-array topology within a memory module. The root element, in this embodiment, is the element having the maximal key value amongst the (k) max heaps as shown. One of ordinary skill in the art would appreciate that while the multiple-tree-array is exemplified herein as a multiple-tree-array comprising min heaps and having a global minimal element, the present invention similarly relates to multiple-tree-array comprising max heaps and having a global maximal element.

In another embodiment, therefore the computer memory system comprises max heaps.

For the purposes of the present invention, “secondary storage” shall mean any data storage system performing slower than typical RAM (Random Access Memory). Secondary Storage typically includes the non-volatile or semi-permanent storage in a computer environment. Common secondary storage devices are diskettes, hard drives, or tapes.

In an embodiment, each specific heap comprising the multiple-tree-array can be configured to operate as a conventional heap, either min- or max-heap. Insertion of a data item into the multiple-tree-array can be performed by invoking an Insert( )procedure upon a specific min heap in the multiple-tree-array with one proviso. If the size of the specific min heap reaches a certain predetermined size threshold, another min heap which is selected for the insertion procedure. In the case where all min heaps reached the predetermined size threshold, additional memory comprising min-heap or max-heap is allocated to the multiple-tree-array memory module.

In one embodiment, said size threshold is in the range of 100-1000 elements, 1000-50000 elements, 50000-100000 elements, or 100000-350000 elements. In one embodiment, the element is a data item as defined above.

Deletion of a data item from the multiple-tree-array which comprises min-heaps can be performed by deleting the global minimum of the multiple-tree-array. As defined, global minimum is the minimal top element which holds the minimal key value in comparison to all (k) min heaps comprising in the multiple-tree-array. Following the deletion of the global minimum, the deleted element is replaced by an element from a specific min heap ensuring the heap invariant. That is ensuring that global minimum is the element which holds the minimal key value in comparison to all (k) min heaps comprising in the multiple-tree-array. Where the last element in a min heap is removed the min heap can be released from the multiple-tree-array memory module. Where all the min heaps in the multiple-tree-array memory module have removed their respective last top element, the entire multiple-tree-array memory module is deemed to be empty or null.

The multiple-tree-array provides storage and retrieval performed at the worst case time of O(k log n), where (k) in the number of heaps managed therein.

An “active min heap” and “active subset of min heaps” shall mean the min heaps which are stored in RAM, and at least one of the min heaps stores the global minimum of the multiple-tree-array. A “passive min heap” and “passive subset of min heaps” shall mean the min heaps which are held in secondary storage.

An “active max heap” and “active subset of max heaps” shall mean the max heaps which are stored in RAM, and at least one of the heaps holds the global maximum of the multiple-tree-array. A “passive max heap” and “passive subset of heaps” shall mean the max heaps which are held in secondary storage.

In another embodiment, an active subset of heaps is held in RAM, while the rest of the heaps are maintained on a secondary storage. In another embodiment, a subset of passive min heaps is maintained on secondary storage. In another embodiment, an active subset of max heaps is held in RAM, while the rest of the heaps are maintained on a secondary storage. In another embodiment, a subset of passive max heaps is maintained on secondary storage.

Special attention should be made for ensuring the dominance of the min heap invariant. Where DeleteMin( ) procedure retrieved and erased the global minimum from the multiple-tree-array, the next global minimum may be located at a passive min heap on secondary storage. Therefore, the min heap invariant cannot be ensured with a current active subset of min heaps. The multiple-tree-array is configured to replace or switch at least one of the active min-heap with at least one passive min heap (one of which is storing the current global minimum).

In one embodiment, a data item in a min heap array shall have at least the following members (i, j,d(i, j)) whereby i and j are pointers to respective templates (of the template matrixes) and the third member is a real number representing the distance between the templates i.e. the 3^(rd) field in the data item is the key, the common field as defined above.

In another embodiment, a template (or a context sequence represented thereby) can be erased or invalidated from the data set during the “life time” of the multiple-tree-array. The invalidation may occur upon merger of templates, as described in the present invention. The merger procedure typically entails invalidation of the merged templates. In such a case, at least one existing data item in the multiple-tree-array (i, j,d(i, j)) may be holding distance information relating to the invalidated template. Therefore, said existing data item requires in turn its invalidation or deletion. Typically, such invalidation would require O(2N) deletions of data items from the multiple-tree-array (N be the number of the cluster).

Therefore, in yet another aspect, the present invention is directed to a postponed deletion procedure or postponed invalidation procedure. The deletion is postponed until the operation of DeleteMin( ). The postponed deletion or invalidation of the data item is delayed until their respective deletion by the operation of DeleteMin( ). In other word, instead of searching for the data item for deletion, the multiple-tree-array “awaits” until the invalidated data item is retrieved, by operation of DeleteMin( ). Following the operation of DeleteMin( ), the retrieved data item (i, j, d(i, j)) is verified to be comprising valid data or valid templates (i) and (j).

By way of non-limiting example, validation procedure utilizes a one dimensional array of Boolean values (B) such that B[i] holds #true if and only if template (i) is of valid status. Alternatively, the validation procedure can utilize an array of other validation information such as but not limited to: a time stamp or a string representing a status.

Section IV

The computer implemented method of the present invention for identifying a sequence template as statistically associated with an attributes set of interest typically comprises: (a) providing a repository of attributes sets, said attributes set is statistically associated with a sequence template; (b) selecting an attributes set of interest; and (c) retrieving at least one sequence template statistically associated with said, attributes set. Typically, a sequence template represents two or more context sequences. Moreover, the attributes set can consist of two or more attributes of interest selected by a user or client. The retrieved sequence template of step (c) typically also represents two or more context sequences. Optionally, retrieved sequence template or cluster represented thereby is a heterogeneous cluster.

In an embodiment, the repository was obtained according to any method of the present invention. In particular, the repository can be obtained by utilization of the LBDL, clustering method. In another embodiment, the repository was obtained by utilization of the VS clustering method. Optionally, the repository is a heterogeneous repository.

Attributes or function attributes of interest can be selected for from the group consisting: the Gene Ontology Project (GO), Interpro annotation (European Molecular Biology Laboratory, EMBL), SMART (a Simple Modular Architecture Research Tool, found at http://smart.embl.de/), UniProt Knowledgebase (SwissProt), OMIM (by NCBI) PROSITE (by the Swiss Institute of Bioinformatics), Protein Information Resource (PIR), GeneCards, and Kyoto Encyclopedia of Genes and Genomes (KEGG).

For the purposes of the present invention, “similarity”, “similarity degree”, or “sd” between any pair of function attributes arrays V, and W can be determined by the following procedure:

sd ← 0 For each a ε {complete - function - attributes - set} { // for each attribute in the complete function attributes array, sum up the differences or divergence between the respective real values if(V[a].value == #true and W[a].value == #false) {sd++;} // as different increase the distance by 1 if(V[a].value==false and W[a].value==true) {sd++;} }

Wherein:

(a)—represents a particular function attribute name; and V[a].value—represents a value associated to particular function (a).

V and W comprises binary digits as values;

Where V and W comprises real numbers as values, “similarity” between any pair of function attributes arrays V, and W can be determined by the following procedure:

sd ← 0 For each a ε {complete - function - attributes - set} { // for each attributes in the function attribute array, sum up the differences or divergence between the respective real values sd+=| V[a].value − W[a].value | }

Wherein:

(a)—represents a particular function attribute name; and

V[a].value—represents a value associated to particular function (a).

One of ordinary skill in the art would understand that either V and/or W may not comprise a particular function attribute. This scenario can be represented as: V[a].value=null i.e. particular function attribute ‘a’ is not associated with V. In such a case, the value may be deemed to have a default value or another symbol which represent a null value.

In an embodiment, where the attributes array features an array of real numbers as values, ‘0.0’ may be deemed to represents a non inclusion of a particular function.

In an embodiment, the above step of (sd+=|V[a].value−W[a].value|) can be performed if and only if (V[a].value !=null and W[a].value !=null). Thus, null valued attributes are ignored.

One of ordinary skill in the art would understand that similarity degree can be determined by the above distance measurement between a pair of function attribute arrays. However, many alternative approaches may be adopted to provide a measure of similarity between function attribute arrays.

For the purposes of the present invention, “functional significance appraisal”, “functional appraisal”, “attribute appraisal” and “functional significance test” shall mean refer to a computational method comprising a statistical test yielding confidence-level or probability, P_value that at least one function attribute is associated with a given gene cluster or gene cluster regulated or otherwise affected by context sequence(s).

The typical input for this computational method is the names or unique identifiers of genes regulated or otherwise affected by the context sequence within a cluster.

The typical result (or output) of functional appraisal is typically a list of attributes which can be deemed as statistically over represented within said input cluster. The list of attributes can further comprise the P_value or confidence level of an attribute within the list.

By way of another non-limiting example, the statistical test can be based on Fisher exact probability test, or hyper-geometric (HG) probability distribution pertaining the sampling without replacement from finite population as explained hereinafter. By way of illustration, N typically denotes the entire size of the gene population (i.e. population size); n denotes the size of context sequence cluster under analysis (i.e. sample size); m denotes the number of genes in the entire population characterized by at least one function attribute (i.e. the “unique” group size); k denotes the number of unique items found in the cluster under analysis. For example, assume N=16,231, n=197, m=678, and k=20 the P_value is therefore 0.0001467. The hypergeometric distribution with parameters N, m and n, and k, can therefore define the probability of getting exactly k genes characterized by said function attribute in a cluster of input genes (or context sequence cluster regulating or affecting them).

Jackknife methodologies and other confidence assisting procedures can be added to increase the confidence level of the enrichment results. Functional appraisal tools can be purchased in, for example, (http://david.abcc.ncifcrf.gov)^(18,19).

In one embodiment, the retrieval of a sequence template statistically associated with an attributes set of interest comprises: determining similarity between the attributes set of interest and each attributes set previously inserted into repository; and retrieving from the repository a sequence template associated with at least one attributes set previously inserted into said repository.

The repository can typically comprise (N) pair(s) of sequence templates and their associated attributes set: <T_(i),AS_(i)>, 1≧i≧N, where T_(i), and AS_(i) are a sequence template and attributes set of the i-th record in the repository, respectively. The method of retrieval of a sequence template statistically associated with an attributes set (AS) of interest, can therefore be performed by: (a) determining similarity, by utilizing similarity formula such as, but not limited to ds(AS,AS_(i)), as defined above; and (b) retrieval of <T_(i),AS_(i)>, 1≧i≧N from the repository together with the respective, ds(AS,AS_(i)).

The order of retrieved records is preferably in descending order according to the similarity degree. The retrieved sequence template typically also represents two or more context sequences. The later may be either identical context sequences or typically context sequence consisting of different sequences.

Moreover, the attributes set associated with the context sequence(s) or sequence template can consist of two or more attributes. Optionally, the context sequence(s) or sequence template may be statistically associated with a particular attribute even where at least one of the context sequence (or gene affected thereby) is not characterized by the attribute. In other words, the retrieval procedures of the present invention therefore enable retrieval of heterogeneous clusters, as defined above.

The retrieval of a sequence template statistically associated with said attributes set, may comprises the steps of: determining similarity between the attributes set of interest and at least one attributes set previously inserted into repository; and retrieving from the repository a sequence template associated with the at least one attributes set previously inserted into said repository.

The repository can therefore typically comprise (N) of pair(s) of sequence templates and their associated attributes set: <T_(i),AS_(i)>, 1≧i≧N, where T_(i), and AS_(i) are a sequence template and attributes set of the i-th record in the repository, respectively. The method of retrieval of a sequence template statistically associated with an attributes set (AS) of interest, can therefore be performed by: (a) determining similarity, by utilizing similarity formula such as, but not limited to ds(AS,AS_(i)), as defined above; and (b) retrieval at least one of <T_(i),AS_(i)>, 1≧i≧N from the repository together with respective ds(AS,AS_(i)). The order of retrieved records is preferably in descending order according to the similarity degree.

The retrieved sequence template typically also represents two or more context sequences. The later may be either identical context sequences or typically context sequence consisting of different sequences.

In the above embodiments, the method typically retrieves at least one sequence template together with a degree of similarity between the attributes set of interest and the attributes set statistically associated with the sequence template. However, filtering of at least one sequence template is typically required.

To that end, in another optional embodiment, the retrieving includes discarding a sequence template associated with said at least one attributes set, where the similarity between said at least one attributes set and the attributes set is above a predefined threshold (L). In that respect therefore, the retrieval further comprises discarding (or filtering out) records having ds(AS,AS_(i))≧(L).

The threshold (L) can be set to various values depending on the number of results sought by the user or the client. As an alternative, the user or client may wish to retrieve the best result alone.

To that end, the retrieving step includes discarding a sequence template associated with said at least one attributes set, where the similarity between said at least one attributes set and the attributes set of interest is above the global minimum. In that respect therefore, the retrieval further comprises discarding (or filtering out) records having ds(AS,AS_(i))>min_(1≧j≧N)(ds(AS,AS_(j))).

In an embodiment, said retrieving includes discarding attributes (i.e. members of the attributes set) where the functional appraisal resulted with a respective P_value greater than 0.3, 0.2, 0.1, or preferably greater than 0.05. The person skilled in the art would appreciate that other P_values ranges can be selected for a particular data set in hand.

Following retrieval of the two or more sequence template statistically associated with said attributes set of interest, the method can further comprise merging at least two of retrieved sequence template (or clusters represented thereby). Merger procedure is detailed above.

Section V Experimental Examples

This Section relates to experimental examples, illustrating the above embodiments of the present invention. These examples are provided for the purpose of illustration only and without any intention of being limiting in any way.

Example 1 Arabidopsis Thaliana

A. Dataset

The complete RefSeq sequences of plants mRNA was downloaded (http://www.ncbi.nlm.nih.gov/RefSeq). The database was filtered in order to exclusively include mRNA sequences of Arabidopsis Thaliana. The dataset was thereafter cleaned of duplicate genes to reduce over representation of identical genes. The translation initiator codon was identified using the RefSeq CDS. Sequence in the length of 9 nucleotides preceding translation initiator codon were parsed, and indexed. The dataset thereafter included the total of 16,491 short sequences of 9 successive nucleotides. The complete dataset was aligned.

B. Application of the LBDL Clustering Method

The LBDL clustering method was applied on the mRNA dataset in 8 separate phases. In each phase the algorithm was provided with a different Lower Bound Distance Limit so as to cluster with varying degree of stringency (0.01; 2.01; 3.01; 4.01; 5.01; 6.01; and 7.01). The separate phase analysis provides an opportunity to investigate smaller more exotic clusters of genes before they merge into larger cluster and lose some significant functional properties along the way.

C. Significant Functional Enrichments of Plant Gene Clusters

Table 1 prescribes the emerging gene clusters which were identified by LBDL clustering method. This table includes selected clusters which demonstrated significant functional attributes.

The clusters in Table 1 are arranged according to size i.e. number of different genes in each cluster. For each cluster, said table provides a template comprising matrix T_(4×9), where the distribution of nucleotides for each position preceding the translation initiation codon. For convenience, the most frequent sequence of successive nucleotides, is disclosed i.e. the dominant context sequence.

The translation initiation codon is at position ‘0’ and does not appear in the table. Table 1 includes a portion of results due the amount of information the LBDL clustering method extracted and collected.

For each template disclosed, the table provides the significant functions or functional attributes set associated with the template. The largest gene cluster includes some 1613 distinct genes. The second largest cluster has 1433 distinct genes. These clusters seem to support previous work which stipulated the A-rich conserved region in higher plants²⁰. The large clusters were enriched, inter alia, with genes encoding nuclear and transcription related proteins, partially in contradiction to previous speculations²¹. Another observation is that the smaller clusters tend to be quite distant from the largest gene clusters. Smaller clusters tend also to include non-A nucleotides with distribution above 80%. For easier reference these nucleotides were highlighted in the body of the table.

As now shown in Table 1, the dominant context sequence ‘tttttaaaa’ is clearly associated for the first time with response to abiotic stimulus and further chemical stimulus. Moreover, a plurality of dominant context sequences are now associated with transcription regulation and transcription in general. For example, templates associated with transcription regulation consists, inter alia, of: ‘aaaaaaaaa’, ‘gttaagaaa’, ‘ttttcttca’ and ‘gagagagaa’. Photosynthesis is associated with ‘acaaaaaca’, and also ‘gaagaagaa’. This unravels the fact that as many as a single function can be associated to a plurality of context sequences or dominant context sequences with strong statistical significance. Table 1 illustrates plurality of other templates and their association with significant functional attributes.

The statistically supported association of functional attribute arrays with a template can be used both in research and genetic engineering.

Example 2 Homo Sapiens

A. Dataset

The complete RefSeq sequences of Human mRNA were downloaded (http://www.ncbi.nlm.nih.gov/RefSeq). The database was filtered in order to exclusively include mRNA sequences of Homo sapiens. The dataset was thereafter cleaned of duplicate genes to reduce over representation of identical genes. The translation initiator codon was identified using the RefSeq CDS. Sequence in the length of 9 nucleotides preceding translation initiator codon were parsed, and indexed. The dataset thereafter included the total of 17,053 short sequences of 9 successive nucleotides. The complete dataset was aligned.

B. Application of the LBDL Clustering Method

The LBDL clustering method was applied on the mRNA dataset in 3 separate phases. In each phase the algorithm was provided with a different Lower Bound Distance Limit so as to cluster with varying degree of stringency (5.01; 6.01; and 7.01). The separate phase analysis provides an opportunity to investigate smaller more exotic clusters of genes before they merge into larger cluster and lose some significant functional properties along the way.

C. Significant Functional Enrichments of Human Gene Clusters

Table 2 prescribes the emerging gene clusters which were identified by LBDL clustering method. This table includes selected clusters which demonstrated significant functional attributes.

The clusters in Table 2 are arranged according to size i.e. number of different genes in each cluster. For each cluster, said table provides a template comprising matrix T_(4×9), where the distribution of nucleotides for each position preceding the translation initiation codon. For convenience, the most frequent sequence of successive nucleotides, is disclosed i.e. the dominant context sequence.

The translation initiation codon is at position ‘0’ and does not appear in the table. Table 2 includes only a portion of the results due the amount of information the LBDL clustering method extracted and collected.

The most significant functional enrichment of each cluster appears as well. The largest gene cluster includes some 1562 distinct genes. The second largest cluster has 987 distinct genes.

Another observation is that the smaller clusters tend to be quite distant from the largest gene clusters.

As now shown in Table 2, the context sequence ‘gccagcacc’ can be associated with response to pest, or pathogen. Importantly the same context sequence is statistically associated with immunoglobulin and the immune system. Moreover, plurality of context sequences are now associated with transcription regulation and transcription in general. For example, templates associated with transcription regulation consists, inter alia, of: ‘cgcgggaag, ‘ggaggaaaa’, and ‘ctgaagsaa’. Metabolism is statistically associated with ‘cccgccgcg’, ‘agcctagaa’ and also ‘ctgaagaaa’. Again, as many as a single function can be associated to a plurality of context sequences with strong statistical significance. Table 2 illustrates plurality of other templates and their association with a significant functional attributes.

The statistically supported associating functional attribute arrays with a template can be used both in research and genetic engineering.

Example 3 Mus Musculus

A. Dataset

The complete RefSeq sequences of Mus Musculus mRNA was downloaded (http://www.ncbi.nlm.nih.gov/RefSeq). The database was filtered in order to exclusively include mRNA sequences of Mus Musculus. The dataset was thereafter cleaned of duplicate genes to reduce over representation of identical genes. The translation initiator codon was identified using the RefSeq CDS. Sequence in the length of 9 nucleotides preceding translation initiator codon were parsed, and indexed. The dataset thereafter included the total of 15,312 short sequences of 9 successive nucleotides. The complete dataset was aligned.

B. Application of the LBDL Clustering Method

The LBDL clustering method was applied on the mRNA dataset in 3 separate phases. In each phase the algorithm was provided with a different Lower Bound Distance Limit so as to cluster with varying degree of stringency (5.01; 6.01; and 7.01). The separate phase analysis provides an opportunity to investigate smaller more exotic clusters of genes before they merge into larger cluster and lose some significant functional properties along the way.

C. Significant Functional Enrichments of Plant Gene Clusters

Table 3 prescribes the emerging gene clusters which were identified by LBDL clustering method. This table includes selected clusters which demonstrated significant functional attributes.

The clusters in Table 3 are arranged according to size i.e. number of different genes in each cluster. For each cluster, said table provides a template comprising matrix T_(4×9), where the distribution of nucleotides for each position preceding the translation initiation codon. For convenience, the most frequent sequence of successive nucleotides, is disclosed i.e. the dominant context sequence.

The translation initiation codon is at position ‘0’ and does not appear in the table. Table 3 includes only a portion of the results due the amount of information the LBDL clustering method extracted and collected.

The most significant functional enrichment of each cluster appears as well. The largest gene cluster includes some 1197 distinct genes. The second largest cluster has 710 distinct genes.

As now shown in Table 3, the context sequence ‘gccgccgcc’ can be associated with sh3 domain. Moreover, plurality of context sequences are now associated with metabolism in general. For example, templates associated with metabolism consists, inter alia, of: ‘ccccgcgcc, and ‘cggaggaag’. Metal ion binding is statistically associated with both ‘gccgccgcc’, and ‘ccccgcgcc’. Again, as many as a single function can be associated to a plurality of context sequences with strong statistical significance. Table 3 illustrates plurality of other templates and their association with a significant functional attributes.

The statistically supported associating functional attribute arrays with a template can be used both in research and genetic engineering.

Example 4 Bos Tauros

A. Dataset

The complete RefSeq sequences of Bos Tauros mRNA was downloaded (http://www.ncbi.nlm.nih.gov/RefSeq). The database was filtered in order to exclusively include mRNA sequences of Bos Tauros. The dataset was thereafter cleaned of duplicate genes to reduce over representation of identical genes. The translation initiator codon was identified using the RefSeq CDS. Sequence in the length of 9 nucleotides preceding translation initiator codon were parsed, and indexed. The dataset thereafter included the total of 9,723 short sequences of 9 successive nucleotides. The complete dataset was aligned.

B. Application of the LBDL Clustering Method

The LBDL clustering method was applied on the mRNA dataset in 3 separate phases. In each phase the algorithm was provided with a different Lower Bound Distance Limit so as to cluster with varying degree of stringency (5.01; 6.01; and 7.01). The separate phase analysis provides an opportunity to investigate smaller more exotic clusters of genes before they merge into larger cluster and lose some significant functional properties along the way.

C. Significant Functional Enrichments of Plant Gene Clusters

Table 4 prescribes the emerging gene clusters which were identified by LBDL clustering method. This table includes selected clusters which demonstrated significant functional attributes.

The clusters in Table 4 are arranged according to size i.e. number of different genes in each cluster. For each cluster, said table depicts the distribution of nucleotides for each position preceding the translation initiation codon. For convenience, the most frequent sequence of successive nucleotides, is disclosed i.e. the dominant context sequence.

The clusters in Table 4 are arranged according to size i.e. number of different genes in each cluster. For each cluster, said table provides a template comprising matrix T_(4×9), where the distribution of nucleotides for each position preceding the translation initiation codon together with the most frequent sequence of successive nucleotides, is disclosed. Table 4 illustrates ,plurality of other templates and their association with a significant functional attributes.

The most significant functional enrichment of each cluster appears as well. The largest gene cluster includes some 815 distinct genes. The second largest cluster has 583 distinct genes.

Example 1-4 exemplify numerous heterogeneous clusters detailed in Tables 1-4 which were identified by the method and systems of the present invention.

TABLE 1 Emerging gene clusters which were identified by the clustering algorithm pertaining Arabidopsis Thaliana. The below clusters are arranged according to declining size. For each cluster, the table depicts the distribution of nucleotides for each position along the context sequence. Size of Cluster Distribution of nucleotides per position (number of context Function attributes set (Enrichment along the context sequence (%) sequences) score/P_value/Benjamini) Pos: −9 −8 −7 1613 membrane (9.88, 7.0E−17, 4.1E−14); transmembrane a a a (9.88, 9.0E−14, 2.2E−11); transit peptide (6.7, 5.5E−12, A % 46.12 71.60 67.45 6.7E−10); chloroplast (6.7, 4.5E−9, 3.0E−7); plastid (6.7, T % 10.78 9.051 1.921 1.7E−6, 7.7E−5); signal (6.14, 4.7E−12, 6.9E−10); G % 33.84 13.63 26.10 glycoprotein (6.14, 3.7E−11, 3.0E−9); response to abiotic C % 9.237 5.703 4.525 stimulus (3.54, 3.3E−6, 3.3E−3); response to chemical stimulus (3.54, 1.1E−5, 7.1E−3); response to hormone stimulus (3.54, 2.2E−3, 4.7E−1); response to endogenous stimulus (3.54, 4.8E−3, 5.9E−1); metal-binding (3.54, 4.6E−12, 8.5E−10); iron (3.54, 8.0E−12, 8.4E−10); oxidoreductase (3.54, 1.1E−11, 1.0E−9); heme (3.54, 2.5E−8, 1.5E−6); monooxygenase (3.54, 3.6E−6, 1.5E−4); E−class P450, group I (3.54, 1.7E−4, 4.9E−1); dna-binding (2.77, 3.4E−4, 1.0E−2); nuclear protein (2.77, 1.4E−3, 3.5E−2); transcription (2.77, 3.4E−3, 6.8E−2); transcription regulation (2.77, 5.2E−3, 9.1E−2); ribonucleoprotein (2.26, 1.2E−9, 8.9E−8); ribosomal protein (2.26, 6.9E−8, 3.9E−6); structural molecule activity (2.26, 4.0E−5, 3.3E−2); structural constituent of ribosome (2.26, 8.9E−4, 3.9E−1); zinc (0.23, 8.8E−2, 5.8E− 1); 1433 transit peptide (5.12, 2.7E−9, 5.0E−7); plastid (5.12, 1.9E− t t t 6, 1.7E−4); chloroplast (5.12, 9.5E−6, 5.8E−4); metal- A % 27.70 9.560 11.16 binding (3.84, 2.9E−13, 2.2E−10); zinc (3.84, 8.4E−9, T % 42.07 50.66 48.98 1.2E−6); zinc-finger (3.84, 3.2E−8, 3.9E−6); response to G % 12.00 28.68 19.46 abiotic stimulus (3.73, 7.1E−6, 7.2E−3); response to C % 18.21 11.09 20.37 chemical stimulus (3.73, 6.4E−5, 4.2E−2); response to stimulus (3.73, 3.8E−4, 1.4E−1); response to endogenous stimulus (3.73, 7.2E−4, 1.9E−1); response to hormone stimulus (3.73, 1.9E−3, 3.5E−1); nuclear protein (3.5, 3.4E−11, 8.2E−9); dna-binding (3.5, 5.4E−6, 3.6E−4); transcription (3.5, 4.2E−4, 1.3E−2); transcription regulation (3.5, 1.5E−3, 3.6E−2); membrane (3.42, 3.4E−6, 2.5E−4); transmembrane (3.42, 1.7E−5, 9.4E−4); signal (2.61, 1.6E−7, 1.6E−5); glycoprotein (2.61, 3.6E−5, 1.8E− 3); translation regulator activity (2.43, 1.5E−4, 1.2E−1); translation factor activity, nucleic acid binding (2.43, 1.5E−4, 1.2E−1); protein biosynthesis (2.43, 2.8E−4, 1.1E− 2); response to external stimulus (2.31, 3.3E−4, 1.5E−1); defense response to pathogen, incompatible interaction (2.31, 4.9E−4, 1.5E−1); response to wounding (2.31, 2.5E− 3, 3.7E−1); response to abscisic acid stimulus (2.17, 3.3E− 3, 4.3E−1); response to water (2.17, 8.6E−3, 6.9E−1); peroxisome (2.05, 4.1E−4, 1.3E−2); gibberellin signaling pathway (1.97, 7.5E−4, 2.2E−2); zinc (1.89, 8.4E−9, 1.2E− 6); zinc-finger (1.89, 3.2E−8, 3.9E−6); Nuclear protein (1.89, 7.5E−2, 5.5E−1); meristem development (1.81, 1.6E−3, 3.3E−1); elongation factor (1.8, 1.6E−3, 3.9E−2); translation elongation factor activity (1.8, 2.0E−3, 5.7E− 1); developmental protein (1.74, 3.2E−4, 1.2E−2); defense response to pathogen, incompatible interaction (1.71, 4.9E−4, 1.5E−1); golgi stack (1.69, 5.4E−3, 1.1E−1); protein transport (1.69, 9.7E−3, 1.8E−1); ribosomal protein (1.64, 2.7E−5, 1.4E−3); 1345 transit peptide (5.07, 2.5E−10, 9.1E−8); mitochondrion g t t (5.07, 1.2E−3, 2.9E−2); membrane (4.47, 3.7E−8, 4.6E−6); A % 15.76 25.27 20.74 transport (4.47, 2.2E−6, 1.4E−4); transmembrane (4.47, T % 31.37 36.05 49.21 2.5E−5, 1.1E−3); plastid (4.05, 1.9E−3, 4.3E−2); amino- G % 38.43 13.75 22.52 acid biosynthesis (2.15, 1.0E−5, 5.4E−4); DNA-directed C % 14.42 24.90 7.509 RNA polymerase activity (2.13, 1.2E−4, 9.3E−2); RNA POLYMERASE (2.13, 4.6E−4, 5.1E−2); PURINE METABOLISM (2.13, 1.9E−2, 5.1E−1); dna-directed rna polymerase (2.13, 2.4E−2, 3.0E−1); PYRIMIDINE METABOLISM (2.13, 2.6E−2, 5.3E−1); ribonucleoprotein (2.03, 2.1E−8, 3.9E−6); ribosomal protein (2.03, 2.8E−8, 4.2E−6); cytosolic ribosome (sensu Eukaryota) (2.03, 2.4E−4, 5.0E−2); structural constituent of ribosome (2.03, 3.5E−3, 6.9E−1); cytosol (2.03, 6.2E−3, 3.6E−1); RIBOSOME (2.03, 4.7E−2, 6.0E−1); nuclear protein (1.84, 5.4E−4, 1.6E−2); transcription (1.84, 1.8E− 2, 2.5E−1); dna-binding (1.84, 6.2E−2, 5.2E−1); transcription regulation (1.84, 7.2E−2, 5.5E−1); glycoprotein (1.8, 3.1E−5, 1.3E−3); signal (1.8, 5.5E−3, 1.1E−1); gtp-binding (1.73, 1.8E−5, 8.3E−4); P-loop (1.73, 6.9E−5, 2.7E−3); nucleotide binding (1.73, 1.5E−4, 5.6E− 3); GTP binding (1.73, 4.0E−3, 8.3E−2); lipoprotein (1.73, 1.0E−2, 1.6E−1); rna-binding (1.58, 1.8E−4, 6.2E−3); metal-binding (1.49, 2.3E−9, 5.7E−7); zinc (1.49, 1.1E−6, 9.6E−5); zinc-finger (1.49, 6.4E−6, 3.9E−4); 751 transit peptide (6.85, 6.3E−11, 2.3E−8); plastid (6.85, a c a 2.9E−8, 7.0E−6); chloroplast (6.85, 2.3E−7, 4.3E−5); A % 60.85 26.76 85.08 transit peptide: Chloroplast (6.85, 9.2E−4, 5.1E−1); T % 14.38 5.326 3.728 apoplast (3.2, 5.7E−6, 5.9E−4); Germin (3.2, 1.8E−5, 7.0E− G % 9.720 32.09 4.127 2); Cupin 1 (3.2, 2.1E−5, 4.2E−2); Cupin region (3.2, C % 15.04 35.81 7.057 3.0E−5, 3.9E−2); signal (3.2, 1.4E−4, 1.3E−2); glycoprotein (3.2, 1.6E−4, 1.3E−2); cell wall (3.2, 5.7E−4, 3.2E−2); manganese (3.2, 6.2E−4, 3.2E−2); metal ion- binding site: Manganese (3.2, 2.2E−3, 5.7E−1); response to stimulus (2.96, 2.5E−5, 5.0E−2); response to abiotic stimulus (2.96, 3.5E−5, 3.5E−2); response to chemical stimulus (2.96, 2.9E−3, 6.2E−1); transmembrane (2.11, 4.4E−4, 2.9E−2); membrane (2.11, 1.2E−3, 4.7E−2); EFh (2, 2.1E−3.5, 2E−1); 680 signal (3.44, 8.0E−6, 5.9E−3); multigene family (3.44, a a t 1.4E−5, 5.0E−3); glycoprotein (3 .44, 7.3E−5, 1.3E−2); A % 46.76 40.44 17.64 oxidoreductase (2.6, 3.3E−5, 8.1E−3); iron (2.6, 7.6E−3, T % 25.44 35.73 35.58 2.7E−1); membrane (2.55, 1.8E−4, 2.2E−2); G % 10.14 10.44 28.38 transmembrane (2.55, 6.6E−4, 4.7E−2); transit peptide C % 17.64 13.38 18.38 (2.21, 7.8E−4, 5.1E−2); plastid (2.21, 1.2E−3, 6.2E−2); chloroplast (2.21, 1.4E−2, 4.2E−1); systemic acquired resistance (2.04, 8.3E−5, 1.6E−1); 680 transit peptide (3.84, 3.5E−7, 1.3E−4); plastid (3.84, 2.4E− a a a 4, 2.1E−2); chloroplast (3.84, 3.5E−4, 2.8E−2); nuclear A % 34.41 48.97 68.08 protein (3.65, 5.6E−5, 8.2E−3); transcription regulation T % 32.64 11.17 10.44 (3.65, 1.7E−4, 2.1E−2); transcription (3.65, 3.6E−4, 2.6E− G % 17.20 23.38 10.58 2); dna-binding (3.65, 6.8E−4, 4.4E−2); chloroplast (3.65, C % 15.73 16.47 10.88 5.0E−5, 2.1E−2); plastid (3.65, 7.5E−5, 1.6E−2); calcium (1.69, 1.2E−3, 6.3E−2); EF hand (1.69, 1.6E−2, 4.2E−1); DNA-binding (1.42, 1.8E−4, 1.9E−2) 655 plastid (, 1.1E−11, 4.8E−9); chloroplast (, 1.2E−11, 2.6E− t t t 9); nuclear protein (3.09, 2.1E−4, 1.5E−2); transcription A % 19.38 7.022 11.45 regulation (3.09, 4.0E−4, 2.7E−2); transcription (3.09, T % 39.23 62.29 43.66 8.4E−4, 5.0E−2); dna-binding (3.09, 6.0E−3, 2.5E−1); G % 21.22 7.175 4.274 transit peptide (1.96, 1.1E−4, 9.8E−3); plastid (1.96, 1.3E− C % 20.15 23.51 40.61 2, 4.5E.1); kinase (1.65, 9.6E−8, 7.0E−5); transferase (1.65, 4.3E−7, 1.6E−4); nucleotide-binding (1.65, 4.5E−6, 1.1E−3); serine/threonine-protein kinase (1.65, 1.1E−5, 1.6E−3); atp-binding (1.65, 2.0E−5, 2.5E−3); auxin signaling pathway (1.46, 1.9E−2, 5.3E−1); zinc-finger (1.09, 7.3E−6, 1.3E−3); zinc (1.09, 8.1E−5, 8.5E−3); metal- binding (1.09, 2.1E−3, 1.1E−1); calcium (0.92, 1.4E−2, 4.5E−1); 618 response to hormone stimulus (3.85, 1.2E−5, 2.5E−2); a a t response to chemical stimulus (3.85, 1.5E−5, 1.5E−2); A % 35.76 53.55 17.63 response to abiotic stimulus (3.85, 2.4E−5, 1.6E−2); T % 29.12 21.03 63.26 response to endogenous stimulus (3.85, 2.9E−4, 1.4E−1); G % 21.19 2.427 6.634 response to auxin stimulus (3.85, 1.9E−3, 4.8E−1); C % 13.91 22.97 12.45 response to stimulus (3.85, 3.0E−3, 5.8E−1); membrane (3.55, 1.6E−7, 5.9E−5); metalloprotein (0.89, 3.3E−2, 5.7E−1); chromoprotein (0.89, 3.9E−2, 6.0E−1); nuclear protein (0.87, 4.0E−2, 6.0E−1); Membrane (0.62, 5.1E−2, 6.4E−1); rna-binding (0.4, 4.2E−2, 5.9E−1); nucleotide- binding (0.12, 1.7E−2, 4.1E−1); 462 nuclear protein (3.2, 2.1E−7, 1.5E−4); transcription (3.2, g a g 3.3E−5, 1.2E−2); transcription regulation (3.2, 4.8E−5, A % 11.47 76.19 15.36 1.2E−2); Transcription factor, K-box (3.2, 2.0E−4, 5.5E− T % 8.225 3.030 7.792 1); domain: K-box (3.2, 2.4E−4, 8.8E−2); domain: MADS- G % 74.89 15.15 74.89 box (3.2, 2.4E−4, 8.8E−2); coiled coil (3.2, 4.0E−4, 4.1E− C % 5.411 5.627 1.948 2); activator (3.2, 6.8E−4, 5.4E−2); dna-binding (3.2, 9.7E−4, 6.9E−2); flowering (3.2, 1.8E−2, 5.9E−1); developmental protein (3.2, 2.2E−2, 6.1E−1); differentiation (3.2, 2.8E−2, 6.8E−1); transport (1.9, 5.7E− 5, 8.4E−3); membrane (1.9, 1.6E−2, 5.7E−1); zinc (0.48, 1.4E−2, 5.5E−1); ion transport (0.42, 2.1E−2, 6.3E−1); transferase (0.38, 2.6E−4, 3.1E−2); 457 response to abiotic stimulus (2.11, 1.1E−3, 6.9E−1); g a a response to chemical stimulus (2.11, 1.7E−3, 6.7E−1); A % 10.50 82.93 96.93 transit peptide (2.02, 1.0E−4, 1.9E−2); chloroplast (2.02, T % 5.032 1.969 0.656 1.1E−2, 3.4E−1); plastid (2.02, 1.2E−2, 3.5E−1); G % 67.83 5.908 1.312 photosynthesis (2.02, 1.4E−2, 3.7E−1); thylakoid (2.02, C % 16.63 9.190 1.094 1.8E−2, 4.1E−1); zymogen (1.95, 9.0E−6, 6.6E−3); propeptide: Activation peptide (1.95, 8.1E−4, 4.6E−1); thiol protease (1.95, 2.4E−3, 1.2E−1); protease (1.95, 2.6E−3, 1.2E−1); signal (1.95, 6.0E−3, 2.2E−1); nuclear protein (1.87, 3.8E−4, 3.9E−2); activator (1.87, 1.1E−3, 6.6E−2); dna-binding (1.87, 1.6E−2, 3.9E−1); transcription (1.87, 2.9E−2, 5.5E−1); transcription regulation (1.87, 3.5E−2, 6.0E−1); oxidoreductase (1.49, 2.1E−5, 5.1E−3); monooxygenase (1.49, 1.9E−4, 2.8E−2); iron (1.49, 8.7E− 4, 5.7E−2); Membrane (1.49, 4.2E−3, 1.8E−1); 375 nuclear protein (1.48, 3.9E−4, 9.1E−2); response to light a a a stimulus (1.44, 3.2E−4, 4.8E−1); response to radiation A % 48.26 29.86 55.46 (1.44, 3.5E−4, 3.0E−1); flavoprotein (1.33, 1.9E−2, 5.8E− T % 16 22.66 3.733 1); oxidoreductase (1.19, 4.0E−3, 3.1E−1); iron (1.19, G % 19.73 21.06 5.333 1.8E−2, 6.1E−1); transit peptide (1.07, 6.9E−3, 4.0E−1); C % 16 26.4 35.46 signal (1.06, 1.4E−3, 1.9E−1); iron (0.89, 1.8E−2, 6.1E−1); metal-binding (0.82, 9.0E−5, 6.4E−2); zinc-finger (0.82, 1.7E−3, 1.9E−1); zinc (0.82, 2.3E−3, 2.1E−1); ribonucleoprotein (0.69, 1.5E−4, 5.5E−2); ribosomal protein (0.69, 8.3E−4, 1.4E−1); protease (0.62, 1.7E−2, 6.2E−1); kinase (0.32, 7.0E−3, 3.7E−1); transferase (0.32, 1.8E−2, 6.0E−1); 327 nuclear protein (1.74, 1.0E−3, 3.2E−1); transcription a a g (1.74, 4.8E−3, 5.1E−1); dna-binding (1.74, 5.5E−3, 4.9E− A % 81.34 58.40 11.62 1); transport (1.48, 7.6E−4, 4.3E−1); gtp-binding (1.28, T % 9.785 0.917 1.529 3.0E−3, 5.2E−1); G % 3.669 30.58 84.40 C % 5.198 10.09 2.446 306 transit peptide (1.44, 9.2E−3, 6.2E−1); transcription t t g regulation (1.43, 8.5E−3, 6.5E−1); dna-binding (0.71, A % 0 30.39 11.11 8.6E−5, 6.1E−2); transcription regulation (0.71, 8.5E−3, T % 59.80 40.52 15.03 6.5E−1); transcription regulation (0.69, 8.5E−3, 6.5E−1); G % 4.248 15.68 45.75 C % 35.94 13.39 28.10 305 multigene family (5.69, 1.4E−9, 1.0E−6); signal (5.69, a a t 5.1E−7, 1.9E−4); toxin (3.16, 8.9E−5, 1.3E−2); plant toxin A % 72.78 66.55 27.86 (3.16, 8.9E−5, 1.3E−2); plant defense (3.16, 3.0E−3, 1.7E− T % 5.245 1.639 36.06 1); membrane (2.9, 5.1E−5, 9.3E−3); transmembrane (2.9, G % 9.180 15.40 11.80 2.4E−4, 2.5E−2); calcium (1.69, 3.3E−4, 3.0E−2); iron C % 12.78 16.39 24.26 (1.69, 5.9E−4, 4.6E−2); oxidoreductase (1.69, 1.1E−3, 7.6E−2); metal-binding (1.69, 2.3E−3, 1.4E−1); hydrogen peroxide (1.69, 3.1E−3, 1.6E−1); 305 cytoplasm (1.72, 1.3E−4, 5.5E−2); chloroplast (1.72, 1.0E− g t a 2, 5.9E−1); nuclear protein (1.31, 1.0E−2, 5.7E−1); A % 26.22 30.16 47.54 transmembrane (1.27, 2.8E−3, 2.6E−1); ribosomal protein T % 21.96 32.78 28.52 (1.05, 4.8E−4, 8.4E−2); ribonucleoprotein (1.05, 7.4E−4, G % 27.86 12.45 5.573 1.0E−1); cytosolic ribosome (sensu Eukaryota) (1.05, C % 23.93 24.59 18.36 7.7E−3, 6.7E−1); eukaryotic 43S preinitiation complex (1.05, 9.2E−3, 6.3E−1); metal-binding (1.01, 1.5E−2, 6.6E− 1); transmembrane (0.89, 2.8E−3, 2.6E−1); 302 chloroplast (5.22, 5.5E−9, 2.4E−6); plastid (5.22, 8.4E−9, t c t 1.8E−6); cytoplasm (5.22, 9.7E−9, 1.4E−6); membrane- A % 6.622 1.986 22.51 bound organelle (5.22, 1.4E−5, 1.5E−3); intracellular T % 70.52 5.960 64.90 membrane-bound organelle (5.22, 2.0E−5, 1.7E−3); G % 8.609 4.635 7.947 organelle (5.22, 6.8E−5, 4.9E−3); C % 14.23 87.41 4.635 245 plastid (1.18, 5.2E−3, 6.8E−1); cytoplasm (1.18, 5.9E−3, t t t 5.7E−1); A % 11.42 2.857 19.59 T % 66.12 91.02 49.38 G % 8.571 3.673 13.06 C % 13.87 2.448 17.95 242 chloroplast (2.61, 3.6E−6, 1.6E−3); plastid (2.61, 4.8E−6, t c t 1.0E−3); cytoplasm (2.61, 1.5E−3, 2.0E−1); membrane- A % 12.80 2.066 17.76 bound organelle (2.61, 6.5E−3, 5.1E−1); intracellular T % 55.37 18.59 78.92 membrane-bound organelle (2.61, 9.0E−3, 5.4E−1); G % 15.28 5.785 0.826 transcription (2.01, 2.1E−3, 4.0E−1); transcription C % 16.52 73.55 2.479 regulation (2.01, 2.6E−3, 3.8E−1); nuclear protein (2.01, A % 4.366 2.620 5.240 1.7E−2, 6.8E−1); gpi-anchor (1.76, 2.7E−4, 1.8E−1); T % 93.88 3.930 83.40 lipoprotein (1.76, 2.1E−3, 5.3E−1); glycoprotein (1.76, G % 0 5.240 0.873 5.6E−3, 4.9E−1); membrane (1.76, 5.7E−3, 4.5E−1); signal C % 1.746 88.20 10.48 (1.76, 8.5E−3, 4.6E−1); anchored to membrane (1.76, 1.4E−2, 6.3E−1); nuclease (1.05, 8.0E−3, 4.8E−1); membrane (0.93, 5.7E−3, 4.5E−1); 219 membrane (2.87, 6.1E−7, 4.5E−4); transmembrane (2.87, a a a 1.9E−3, 1.8E−1); transport (2.87, 5.4E−3, 3.9E−1); signal A % 51.59 94.06 51.59 (2.27, 3.2E−6, 7.8E−4); glycoprotein (2.27, 4.9E−4, 6.9E− T % 10.50 1.369 10.50 2); cell wall (sensu Magnoliophyta) (1.5, 2.0E−3, 2.5E−1); G % 17.35 4.109 23.28 cell wall (1.5, 1.0E−2, 6.7E−1); external encapsulating C % 20.54 0.456 14.61 structure (1.5, 1.1E−2, 6.2E−1); metal-binding (0.97, 1.1E− 3, 1.2E−1); transport (0.68, 5.4E−3, 3.9E−1); coiled coil (0.21, 1.4E−2, 6.8E−1); 196 transcription (2.65, 7.7E−4, 4.3E−1); transcription c a c regulation (2.65, 9.4E−4, 2.9E−1); nuclear protein (2.65, A % 18.36 53.57 21.42 2.4E−3, 4.4E−1); plastid (1.56, 1.0E−2, 6.5E−1); T % 7.653 36.22 23.97 G % 29.59 5.102 26.02 C % 44.38 5.102 28.57 187 membrane (1.5, 1.6E−3, 6.9E−1); transmembrane (1.5, g a t 7.5E−3, 6.7E−1); chaperone (1.23, 6.3E−3, 6.9E−1); A % 35.82 44.91 10.16 T % 1.604 43.85 73.26 G % 50.26 4.812 1.069 C % 12.29 6.417 15.50 176 transit peptide (2.61, 1.9E−5, 1.4E−2); lyase (2.61, 1.4E−4, g a a 4.9E−2); Ribulose bisphosphate carboxylase, small chain A % 14.20 88.06 92.61 (2.61, 1.6E−4, 4.7E−1); carbon dioxide fixation (2.61, T % 2.840 1.704 0.568 4.8E−4, 1.1E−1); photorespiration (2.61, 6.3E−4, 1.1E−1); G % 81.81 7.954 6.25 photosynthesis (2.61, 1.2E−3, 1.6E−1); chloroplast (2.61, C % 1.136 2.272 0.568 1.7E−3, 1.8E−1); GLYOXYLATE AND DICARBOXYLATE METABOLISM (2.61, 2.9E−3, 2.8E−1); multigene family (2.61, 4.1E−3, 3.1E−1); plastid (2.61, 4.6E−3, 3.1E−1); cytoplasm (1.35, 1.1E−3, 1.5E−1); 165 nuclear protein (1.99, 7.5E−4, 4.2E−1); chloroplast (1.58, c t t 4.7E−3, 6.8E−1); nuclear protein (0.59, 7.5E−4, 4.2E−1); A % 9.090 4.848 4.242 T % 24.84 90.30 89.69 G % 6.060 3.636 1.818 C % 60 1.212 4.242 162 metal-binding (1.1, 6.8E−4, 3.9E−1); zinc (0.62, 2.5E−3, t g a 6.0E−1); kinase (0.61, 4.4E−3, 6.6E−1); A % 31.48 17.90 69.75 T % 43.20 24.69 10.49 G % 12.96 48.14 15.43 C % 12.34 9.259 4.320 161 dna-binding (2.13, 1.2E−3, 5.7E−1); nuclear protein (2.13, t t a 2.0E−3, 5.1E−1); A % 19.87 26.08 50.93 T % 73.29 45.96 32.91 G % 3.105 3.105 10.55 C % 3.726 24.84 5.590 160 membrane (1.72, 3.9E−4, 2.5E−1); plastid (0.77, 2.6E−3, t c t 6.7E−1); chloroplast (0.77, 4.7E−3, 6.4E−1); A % 11.87 11.25 0.625 T % 78.75 23.75 96.87 G % 6.25 6.25 1.875 C % 3.125 58.75 0.625 157 chloroplast (2.62, 2.0E−4, 8.3E−2); plastid (2.62, 2.4E−4, a a g 5.1E−2); membrane-bound organelle (2.62, 7.8E−4, 1.1E− A % 64.96 59.23 31.84 1); intracellular membrane-bound organelle (2.62, 1.3E− T % 12.73 5.732 6.369 3, 1.3E−1); organelle (2.62, 2.0E−3, 1.6E−1); cytoplasm G % 8.917 28.66 36.30 (2.62, 2.7E−3, 1.8E−1); intracellular organelle (2.62, 3.3E− C % 13.37 6.369 25.47 3, 1.8E−1); intracellular (2.62, 7.6E−3, 3.4E−1); 155 ubiquitin-protein ligase activity (1.21, 1.2E−3, 6.4E−1); g a t A % 18.06 35.48 3.225 T % 25.80 29.03 89.67 G % 38.06 21.93 6.451 C % 18.06 13.54 0.645 152 transmembrane (1.61, 3.1E−3, 5.3E−1); t t g glycosyltransferase (1.11, 5.3E−3, 6.3E−1); metal-binding A % 3.947 15.78 15.13 (0.47, 8.1E−4, 4.5E−1); T % 94.07 42.10 3.289 G % 1.973 3.289 46.71 C % 0 38.81 34.86 150 membrane (1.9, 1.3E−3, 6.1E−1); membrane (1.34, 1.3E− t t a 3, 6.1E−1); nucleotide-binding (0.44, 1.9E−3, 5.1E−1); A % 2 9.333 35.33 T % 97.33 48.66 27.33 G % 0.666 18.66 27.33 C % 0 23.33 10 139 ribosomal protein (2.22, 9.5E−6, 7.0E−3); intracellular c c g non-membrane-bound organelle (2.22, 1.1E−5, 4.6E−3); A % 15.10 1.438 0 non-membrane-bound organelle (2.22, 1.1E−5, 4.6E−3); T % 26.61 0 2.877 ribonucleoprotein complex (2.22, 1.1E−5, 1.6E−3); G % 2.158 0 96.40 ribosome (2.22, 1.1E−4, 1.2E−2); structural constituent of C % 56.11 98.56 0.719 ribosome (2.22, 1.5E−4, 2.2E−1); structural molecule activity (2.22, 2.2E−4, 1.7E−1); ribonucleoprotein (2.22, 2.9E−4, 1.0E−1); ribosome (2.22, 6.0E−3, 6.7E−1); RIBOSOME (2.22, 6.7E−3, 5.3E−1); ribosomal protein (2.21, 9.5E−6, 7.0E−3); RIBOSOME (2.21, 6.7E−3, 5.3E− 1); 131 threonine protease (2.08, 7.9E−4, 4.4E−1); a a t A % 70.22 96.18 35.11 T % 6.870 1.526 45.03 G % 17.55 1.526 12.97 C % 5.343 0.763 6.870 120 wd repeat (2.32, 1.9E−4, 1.3E−1); WD40 (2.32, 2.1E−3, a t c 5.2E−1); transferase (0.68, 1.9E−3, 5.0E−1); A % 46.66 11.66 10.83 T % 14.16 37.5 18.33 G % 34.16 14.16 17.5 C % 5 36.66 53.33 117 metal-binding (1.95, 5.6E−4, 3.4E−1); nuclear protein a t c (1.46, 1.7E−3, 4.6E−1); signal (1.15, 2.3E−3, 4.3E−1); A % 66.66 5.128 21.36 T % 17.09 65.81 4.273 G % 6.837 21.36 0.854 C % 9.401 7.692 73.50 109 intracellular (1.56, 4.3E−3, 6.1E−1); g c g A % 27.52 14.67 20.18 T % 21.10 19.26 15.59 G % 37.61 5.504 64.22 C % 13.76 60.55 0 108 chloroplast (4.2, 6.9E−6, 3.0E−3); plastid (4.2, 8.5E−6, t c t 1.8E−3); intracellular membrane-bound organelle (4.2, A % 4.629 1.851 12.96 3.3E−5, 4.8E−3); membrane-bound organelle (4.2, 3.8E−5, T % 71.29 14.81 62.03 4.1E−3); intracellular organelle (4.2, 6.5E−5, 5.6E−3); G % 20.37 15.74 22.22 intracellular (4.2, 6.7E−5, 4.8E−3); organelle (4.2, 7.4E−5, C % 3.703 67.59 2.777 4.5E−3); cytoplasm (4.2, 2.0E−4, 1.1E−2); cell (4.2, 3.2E− 3, 1.4E−1); OXIDATIVE PHOSPHORYLATION (0.79, 6.7E−3, 5.3E−1); 104 response to abiotic stimulus (3.87, 2.9E−6, 6.0E−3); t c t response to stimulus (3.87, 6.4E−5, 6.3E−2); A % 5.769 4.807 36.53 T % 83.65 3.846 40.38 G % 6.730 2.884 22.11 C % 3.846 88.46 0.961 100 transit peptide (1.65, 2.9E−3, 4.2E−1); metal-binding t c t (1.43, 6.7E−3, 6.2E−1); ribonucleoprotein (0.96, 7.5E−4, A % 19 3 8 4.2E−1); ribosomal protein (0.96, 1.9E−3, 5.1E−1); metal- T % 42 7 90 binding (0.44, 6.7E−3, 6.2E−1); G % 31 5 0 C % 8 85 2 93 multigene family (3.38, 1.7E−6, 1.3E−3); calmodulin- g t t binding (3.38, 1.1E−5, 3.9E−3); membrane (3.38, 7.5E−4, A % 17.20 16.12 0 8.8E−2); transmembrane (3.38, 7.0E−3, 4.7E−1); zinc T % 17.20 54.83 98.92 (2.79, 2.9E−4, 5.2E−2); alternative splicing (2.79, 7.3E−4, G % 50.53 25.80 0 1.0E−1); metal-binding (1.19, 8.2E−5, 2.0E−2); zinc (1.19, C % 15.05 3.225 1.075 2.9E−4, 5.2E−2); zinc-finger (1.19, 1.1E−3, 1.1E−1); 85 intracellular membrane-bound organelle (3.4, 5.0E−5, t c t 2.1E−2); membrane-bound organelle (3.4, 5.6E−5, 1.2E− A % 4.705 3.529 20 2); cytoplasm (3.4, 2.0E−4, 2.8E−2); intracellular T % 91.76 2.352 77.64 organelle (3.4, 2.0E−4, 2.1E−2); organelle (3.4, 2.2E−4, G % 0 1.176 2.352 1.9E−2); intracellular (3.4, 1.8E−3, 1.2E−1); cytoplasm C % 3.529 92.94 0 (2.65, 2.0E−4, 2.8E−2); chloroplast (2.65, 7.3E−3, 3.3E−1); plastid (2.65, 8.0E−3, 3.2E−1); TIR (1.04, 6.1E−3, 6.6E−1); 81 lipid biosynthesis (1.91, 5.5E−4, 6.7E−1); a a a A % 44.44 75.30 95.06 T % 20.98 20.98 1.234 G % 30.86 3.703 3.703 C % 3.703 0 0 67 zinc-finger (1.22, 9.0E−4, 4.8E−1); zinc (1.22, 1.8E−3, c a t 4.9E−1); A % 22.38 52.23 13.43 T % 16.41 38.80 85.07 G % 26.86 8.955 1.492 C % 34.32 0 0 67 envelope (1.09, 4.6E−3, 6.4E−1); c a t A % 44.77 71.64 7.462 T % 4.477 17.91 88.05 G % 2.985 4.477 1.492 C % 47.76 5.970 2.985 61 TERPENOID BIOSYNTHESIS (2.15, 3.9E−3, 3.6E−1); c g a BIOSYNTHESIS OF STEROIDS (2.15, 1.5E−2, 5.8E−1); A % 8.196 4.918 59.01 T % 18.03 6.557 34.42 G % 22.95 70.49 4.918 C % 50.81 18.03 1.639 54 RNA POLYMERASE (2.11, 1.9E−3, 1.9E−1); t a g PYRIMIDINE METABOLISM (2.11, 1.3E−2, 5.3E−1); A % 1.851 55.55 14.81 PURINE METABOLISM (2.11, 1.8E−2, 5.0E−1); T % 79.62 40.74 3.703 G % 16.66 1.851 81.48 C % 1.851 1.851 0 52 ribonucleoprotein (0.95, 8.9E−4, 2.8E−1); t c t A % 15.38 0 5.769 T % 59.61 0 67.30 G % 13.46 0 3.846 C % 11.53 100 23.07 47 transport (1.34, 2.0E−4, 1.4E−1); g a a A % 0 89.36 87.23 T % 0 0 0 G % 100 2.127 12.76 C % 0 8.510 0 46 cell (1.99, 5.3E−3, 6.9E−1); plastid (1.99, 5.6E−3, 5.6E−1); t c g intracellular organelle (1.99, 9.8E−3, 4.6E−1); organelle A % 23.91 8.695 8.695 (1.99, 1.0E−2, 4.3E−1); intracellular (1.99, 1.1E−2, 4.0E− T % 41.30 10.86 10.86 1); intracellular membrane-bound organelle (1.99, 1.5E− G % 15.21 2.173 67.39 2, 4.8E−1); membrane-bound organelle (1.99, 1.5E−2, C % 19.56 78.26 13.04 4.6E−1); cytoplasm (1.99, 3.3E−2, 6.7E−1); membrane- enclosed lumen (1.37, 5.8E−3, 3.9E−1); organelle lumen (1.37, 5.8E−3, 3.9E−1); nucleolus (1.37, 9.4E−3, 4.9E−1); nuclear lumen (1.37, 2.6E−2, 6.1E−1); 43 heat shock (2.56, 1.4E−3, 4.0E−1); t a a A % 4.651 90.69 65.11 T % 53.48 0 6.976 G % 13.95 4.651 23.25 C % 27.90 4.651 4.651 43 eukaryotic 43S preinitiation complex (1.5, 2.9E−5, 1.3E− t t t 2); cytosolic small ribosomal subunit (sensu Eukaryota) A % 4.651 0 0 (1.5, 1.0E−3, 1.4E−1); eukaryotic 48S initiation complex T % 81.39 58.13 100 (1.5, 1.0E−3, 1.4E−1); protein complex (1.5, 2.0E−3, 1.9E− G % 4.651 2.325 0 1); small ribosomal subunit (1.5, 3.1E−3, 2.3E−1); C % 9.302 39.53 0 cytosolic ribosome (sensu Eukaryota) (1.5, 5.2E−3, 3.1E−1); 42 disulfide bond (1.35, 1.5E−3, 6.9E−1); g a g A % 2.380 88.09 11.90 T % 21.42 4.761 0 G % 66.66 2.380 80.95 C % 9.523 4.761 7.142 42 transcription regulation (1.4, 4.2E−3, 6.5E−1); nuclear a a a protein (1.4, 5.5E−3, 6.4E−1); metal-binding (1.4, 6.5E−3, A % 95.23 38.09 88.09 6.2E−1); nuclear protein (0.47, 5.5E−3, 6.4E−1); T % 2.380 14.28 7.142 G % 0 26.19 4.761 C % 2.380 21.42 0 40 atp-binding (1.01, 1.5E−3, 6.6E−1); nucleotide-binding gc t c (1.01, 2.2E−3, 5.5E−1); A % 15 0 10 T % 10 95 0 G % 37.5 0 2.5 C % 37.5 5 87.5 36 cytoplasm (2.29, 3.6E−4, 1.4E−1); mitochondrion (2.29, tg a c 5.2E−4, 1.1E−1); intracellular membrane-bound organelle A % 0 72.22 0 (2.29, 4.7E−3, 4.9E−1); membrane-bound organelle (2.29, T % 38.88 0 0 4.9E−3, 4.1E−1); intracellular organelle (2.29, 8.7E−3, G % 38.88 27.77 0 4.7E−1); organelle (2.29, 9.1E−3, 4.3E−1); intracellular C % 22.22 0 100 (2.29, 2.3E−2, 6.1E−1); ribosome (0.87, 7.8E−3, 4.9E−1); non-membrane-bound organelle (0.87, 1.1E−2, 4.1E−1); intracellular non-membrane-bound organelle (0.87, 1.1E− 2, 4.1E−1); ribonucleoprotein complex (0.87, 1.9E−2, 5.6E−1); 35 chloroplast stroma (1.04, 1.1E−3, 3.9E−1); plastid stroma g ag t (1.04, 2.1E−3, 3.7E−1); A % 40 40 11.42 T % 2.857 2.857 88.57 G % 48.57 40 0 C % 8.571 17.14 0 35 membrane (1.78, 4.1E−6, 3.0E−3); transmembrane (1.78, g a c 4.9E−5, 1.8E−2); transmembrane region (1.78, 7.3E−4, A % 11.42 34.28 0 4.3E−1); ubl conjugation pathway (1.78, 1.4E−3, 2.8E−1); T % 28.57 31.42 0 metal-binding (1.78, 2.0E−3, 3.0E−1); zinc (1.78, 5.1E−3, G % 34.28 22.85 0 5.2E−1); C % 25.71 11.42 100 34 transcription factor activity (1.03, 2.5E−4, 3.3E−1); t t c transcription regulator activity (1.03, 7.7E−4, 4.7E−1); A % 0 0 0 T % 94.11 94.11 5.882 G % 0 5.882 2.941 C % 5.882 0 91.17 32 prenylation (2.78, 3.3E−5, 2.4E−2); lipid moiety-binding a c g region: S-geranylgeranyl cysteine (2.78, 1.0E−4, 7.5E−2); A % 62.5 6.25 3.125 lipoprotein (2.78, 4.9E−4, 1.6E−1); nucleotide phosphate- T % 0 0 9.375 binding region: GTP (2.78, 1.4E−3, 4.1E−1); gtp-binding G % 34.37 0 84.37 (2.78, 2.0E−3, 3.9E−1); membrane (2.78, 4.7E−3, 5.8E−1); C % 3.125 93.75 3.125 32 membrane (2.21, 8.0E−6, 5.9E−3); transmembrane (2.21, g ag c 9.5E−5, 3.4E−2); metal-binding (2.21, 2.6E−4, 6.2E−2); A % 12.5 37.5 0 zinc (2.21, 5.2E−4, 9.1E−2); transmembrane region (2.21, T % 25 12.5 0 7.3E−4, 4.3E−1); ubl conjugation pathway (2.21, 1.7E−3, G % 56.25 37.5 0 2.2E−1); zinc-finger (2.21, 4.5E−3, 4.2E−1); C % 6.25 12.5 100 28 SF016605: Arabidopsis thaliana transcription factor c t c DREB1B (2.02, 2.8E−5, 9.8E−2); DNA-binding A % 10.71 0 0 region: AP2/ERF (2.02, 9.4E−5, 7.0E−2); transcription T % 39.28 85.71 28.57 factor (2.02, 2.7E−4, 1.8E−1); activator (2.02, 8.7E−4, G % 7.142 14.28 14.28 2.7E−1); nuclear protein (2.02, 1.6E−3, 3.3E−1); C % 42.85 0 57.14 28 nuclear protein (3.28, 8.0E−5, 5.7E−2); transcription t t t (3.28, 2.0E−4, 7.1E−2); transcription regulation (3.28, A % 0 0 3.571 2.2E−4, 5.3E−2); T % 53.57 100 96.42 G % 17.85 0 0 C % 28.57 0 0 27 response to water deprivation (1.91, 3.3E−4, 4.9E−1); c t t response to water (1.91, 5.0E−4, 4.0E−1); A % 0 0 14.81 T % 0 96.29 51.85 G % 7.407 3.703 29.62 C % 92.59 0 3.703 26 response to chemical stimulus (2.9, 3.0E−4, 4.6E−1); a a c response to hormone stimulus (2.9, 6.2E−4, 4.7E−1); A % 84.61 80.76 15.38 response to abiotic stimulus (2.9, 9.7E−4, 4.8E−1); T % 11.53 19.23 3.846 G % 3.846 0 19.23 C % 0 0 61.53 26 nucleotide-binding (1.12, 2.2E−3, 5.5E−1); transferase t t g (1.12, 2.7E−3, 4.9E−1); A % 0 0 0 T % 92.30 88.46 19.23 G % 7.692 0 69.23 C % 0 11.53 11.53 25 ubiquitin conjugating enzyme activity (1.05, 2.4E−4, c t c 3.3E−1); small protein conjugating enzyme activity (1.05, A % 0 4 4 2.8E−4, 2.0E−1); UBCc (1.05, 1.7E−3, 4.5E−1); T % 32 92 0 G % 8 4 0 C % 60 0 96 25 nuclear protein (2.56, 1.0E−3, 5.2E−1); transcription t t t regulation (2.56, 4.7E−3, 6.9E−1); A % 0 0 4 T % 56 100 96 G % 20 0 0 C % 24 0 0 25 Protein phosphatase 2C (2, 2.3E−4, 6.0E−1); PP2Cc (2, a g a 3.2E−3, 6.8E−1); A % 96 16 100 T % 0 0 0 G % 0 80 0 C % 4 4 0 24 pyridoxal phosphate (3.68, 9.5E−7, 7.0E−4); t g t nicotianamine synthase activity (3.68, 1.7E−6, 2.9E−3); A % 20.83 0 0 Nicotianamine synthase (3.68, 2.0E−6, 8.1E−3); multigene T % 45.83 16.66 100 family (3.68, 1.1E−3, 3.4E−1); transferase activity, G % 0 62.5 0 transferring alkyl or aryl (other than methyl) groups C % 33.33 20.83 0 (3.68, 1.4E−3, 6.8E−1); Size of Cluster Distribution of nucleotides per position (number of context along the context sequence (%) sequences) −6 −5 −4 −3 −2 −1 1613 a a a a a a 61.50 74.27 73.03 89.77 79.54 71.48 3.967 4.091 1.735 1.735 2.603 7.873 28.58 6.943 23.49 7.501 6.137 15.93 5.951 14.69 1.735 0.991 11.71 4.711 1433 t t a a a a 19.05 29.09 73.83 65.10 57.50 50.03 46.12 33.63 9.141 8.374 10.81 12.63 24.49 23.23 10.53 23.86 3.838 23.16 10.32 14.02 6.489 2.651 27.84 14.16 1345 a a g a a a 42.08 41.48 26.54 72.26 73.53 49.07 30.85 29.07 12.93 1.040 9.293 5.204 14.20 12.56 51.59 24.83 12.11 31.15 12.86 16.87 8.921 1.858 5.055 14.57 751 a a a a c a 77.36 38.88 94.67 84.68 32.75 79.62 6.125 1.731 0.665 5.592 2.263 2.396 12.91 31.29 2.396 7.856 9.986 11.05 3.595 28.09 2.263 1.864 54.99 6.924 680 g a g a a a 23.38 44.11 8.382 91.17 48.23 77.5 20.58 27.79 7.941 2.794 34.11 6.470 34.70 5.882 66.91 3.676 11.17 12.94 21.32 22.20 16.76 2.352 6.470 3.088 680 t c a g c a 8.088 6.323 51.61 24.11 36.17 62.20 74.11 42.35 26.91 18.67 7.058 10.88 11.61 3.235 5.147 47.94 9.117 8.970 6.176 48.08 16.32 9.264 47.64 17.94 655 t c t t c a 4.122 5.496 9.770 8.396 14.80 52.97 81.37 24.27 69.31 34.96 30.99 7.328 10.83 11.29 6.870 23.66 3.816 9.465 3.664 58.93 14.04 32.97 50.38 30.22 618 c a a a c a 10.51 44.17 80.09 72.00 9.385 59.87 15.37 22.00 3.883 19.41 17.31 7.766 12.94 5.177 11.00 7.443 5.987 7.443 61.16 28.64 5.016 1.132 67.31 24.91 462 a g a g a a 93.07 5.411 95.45 20.34 89.39 41.12 5.844 1.731 2.380 1.731 1.298 18.83 0 88.52 1.731 75.32 3.896 35.93 1.082 4.329 0.432 2.597 5.411 4.112 457 g a a a a a 12.03 75.92 65.86 50.98 52.29 53.61 1.750 4.814 14.66 10.06 7.877 12.91 84.68 2.625 15.97 29.32 8.752 13.34 1.531 16.63 3.501 9.628 31.07 20.13 375 a t c a c c 77.06 24.26 1.866 62.93 5.866 13.06 14.93 54.4 5.6 9.6 30.93 12.26 4.266 1.333 2.133 24.53 1.333 8.8 3.733 20 90.4 2.933 61.86 65.86 327 a a g a a g 93.57 85.32 1.529 90.82 70.94 11.31 2.446 0.305 0.611 0.305 6.727 3.975 1.834 9.785 96.94 8.562 1.834 82.56 2.140 4.587 0.917 0.305 20.48 2.140 306 a a a a a a 84.96 55.22 88.56 93.13 83.66 66.66 3.921 16.01 1.633 0.653 1.633 5.228 1.307 14.70 4.248 3.921 2.941 19.28 9.803 14.05 5.555 2.287 11.76 8.823 305 c a a a a a 24.91 77.37 92.45 96.06 62.29 42.95 4.262 4.262 2.622 0 26.55 12.78 0.327 10.16 2.295 2.950 6.557 9.508 70.49 8.196 2.622 0.983 4.590 34.75 305 t c a a t c 15.40 23.93 78.03 68.19 19.67 1.967 67.21 19.01 1.311 7.540 57.04 1.967 8.196 5.245 9.508 16.39 1.967 0.983 9.180 51.80 11.14 7.868 21.31 95.08 302 t c a t c a 1.655 1.324 40.72 19.20 0.662 66.88 60.92 0.993 27.81 44.70 2.649 3.311 9.602 2.649 29.47 28.47 0 18.54 27.81 95.03 1.986 7.615 96.68 11.25 245 t c a a a a 22.85 6.530 74.69 85.30 90.61 74.28 55.91 22.85 3.673 0.408 3.265 1.632 18.36 28.16 18.77 11.42 3.265 22.44 2.857 42.44 2.857 2.857 2.857 1.632 242 t c a t c a 3.305 1.239 47.93 18.18 0 85.95 72.72 2.066 42.14 72.72 2.066 4.958 8.264 2.892 9.090 2.479 0 4.132 15.70 93.80 0.826 6.611 97.93 4.958 17.90 7.860 17.03 16.59 20.08 24.01 13.97 83.40 36.24 50.21 11.35 17.03 24.89 0.873 3.056 27.51 6.550 23.14 43.23 7.860 43.66 5.676 62.00 35.80 219 a t c a a a 82.19 34.70 2.283 97.71 95.89 70.31 5.936 39.26 2.739 0 0.913 17.35 2.739 11.41 7.762 0.913 0.913 2.739 9.132 14.61 87.21 1.369 2.283 9.589 196 t c t c t a 10.20 5.102 2.040 3.061 8.673 37.24 58.67 29.59 93.87 10.20 65.30 18.36 8.673 16.32 1.020 21.93 4.081 29.08 22.44 48.97 3.061 64.79 21.93 15.30 187 t a g a a g 6.951 42.24 29.94 88.77 99.46 3.208 41.71 9.625 4.278 1.069 0 0.534 32.08 8.021 60.42 4.812 0 93.58 19.25 40.10 5.347 5.347 0.534 2.673 176 g a a g a a 1.704 71.59 87.5 1.136 58.52 39.20 1.704 7.386 1.704 2.272 15.34 8.522 96.02 13.63 6.818 96.59 2.272 25 0.568 7.386 3.977 0 23.86 27.27 165 c t t t c ac 2.424 9.696 3.030 10.90 9.090 32.12 31.51 62.42 55.15 44.84 36.36 5.454 2.424 3.030 0.606 19.39 5.454 30.30 63.63 24.84 41.21 24.84 49.09 32.12 162 g t t t t a 24.07 17.90 8.641 1.851 7.407 38.88 8.641 40.74 85.80 88.88 57.40 8.024 64.19 17.28 3.703 8.024 3.703 32.71 3.086 24.07 1.851 1.234 31.48 20.37 161 g c a g a g 26.08 17.39 47.20 43.47 93.78 3.105 1.242 2.484 9.937 8.074 4.968 1.863 47.82 3.105 34.16 48.44 1.242 65.21 24.84 77.01 8.695 0 0 29.81 160 c t a a a a 3.125 13.12 33.75 74.37 51.25 94.37 3.125 53.12 12.5 13.12 4.375 0.625 1.25 3.125 25 5.625 9.375 3.75 92.5 30.62 28.75 6.875 35 1.25 157 a t t c a g 57.32 26.11 1.273 1.273 64.96 29.93 35.66 50.95 98.72 14.64 4.458 9.554 5.095 3.821 0 8.917 0.636 56.05 1.910 19.10 0 75.15 29.93 4.458 155 t t t g a a 0 1.290 7.096 3.225 77.41 67.74 90.96 92.90 49.03 1.935 12.90 13.54 1.935 2.580 27.09 90.96 1.935 14.19 7.096 3.225 16.77 3.870 7.741 4.516 152 t t c a a a 3.947 19.73 11.18 46.05 69.07 61.18 78.94 64.47 18.42 14.47 23.02 7.894 7.894 9.868 30.26 29.60 4.605 21.05 9.210 5.921 40.13 9.868 3.289 9.868 150 g t g a c g 10 28.66 1.333 71.33 25.33 22.66 0.666 50 29.33 2 19.33 28 81.33 8.666 48.66 20 11.33 36.66 8 12.66 20.66 6.666 44 12.66 139 g c g a a a 23.02 23.02 16.54 80.57 60.43 67.62 4.316 6.474 0.719 7.913 17.26 15.10 69.78 2.877 79.13 10.07 9.352 13.66 2.877 67.62 3.597 1.438 12.94 3.597 131 c t g a a a 25.19 7.633 0.763 82.44 94.65 61.06 31.29 51.90 3.053 1.526 0 9.923 11.45 37.40 93.12 13.74 2.290 21.37 32.06 3.053 3.053 2.290 3.053 7.633 120 g g a a a a 19.16 3.333 95 88.33 46.66 70 14.16 0.833 0.833 5.833 23.33 4.166 51.66 95 2.5 5 14.16 7.5 15 0.833 1.666 0.833 15.83 18.33 117 a t c a t c 75.21 0.854 5.982 88.88 3.418 0 12.82 92.30 3.418 2.564 70.94 0.854 7.692 0 0.854 3.418 0 0 4.273 6.837 89.74 5.128 25.64 99.14 109 a c g g c g 61.46 3.669 0.917 40.36 37.61 8.256 3.669 5.504 2.752 1.834 0.917 2.752 29.35 0 96.33 55.96 0 85.32 5.504 90.82 0 1.834 61.46 3.669 108 t g a g t g 12.96 9.259 83.33 0 25 43.51 85.18 25 3.703 1.851 39.81 11.11 1.851 56.48 8.333 97.22 5.555 44.44 0 9.259 4.629 0.925 29.62 0.925 104 g a a a a a 0.961 93.26 86.53 97.11 90.38 69.23 32.69 3.846 3.846 0 0.961 7.692 63.46 0.961 1.923 0.961 1.923 19.23 2.884 1.923 7.692 1.923 6.730 3.846 100 t c g a a a 9 37 1 97 86 82 66 6 1 0 0 5 21 9 72 2 5 8 4 48 26 1 9 5 93 g a a g a a 9.677 89.24 88.17 5.376 95.69 89.24 12.90 0 5.376 2.150 1.075 4.301 48.38 9.677 5.376 86.02 0 3.225 29.03 1.075 1.075 6.451 3.225 3.225 85 c t t t c t 7.058 2.352 4.705 17.64 17.64 29.41 4.705 95.29 60 70.58 3.529 34.11 10.58 1.176 1.176 7.058 1.176 15.29 77.64 1.176 34.11 4.705 77.64 21.17 81 a t c a c c 88.88 32.09 2.469 39.50 7.407 9.876 3.703 59.25 1.234 22.22 9.876 8.641 1.234 2.469 2.469 33.33 1.234 27.16 6.172 6.172 93.82 4.938 81.48 54.32 67 t t g g a a 0 0 4.477 0 91.04 62.68 68.65 98.50 5.970 1.492 4.477 29.85 28.35 0 65.67 98.50 2.985 5.970 2.985 1.492 23.88 0 1.492 1.492 67 c a a t c c 1.492 80.59 92.53 40.29 2.985 23.88 0 4.477 4.477 47.76 2.985 5.970 0 8.955 0 11.94 0 17.91 98.50 5.970 2.985 0 94.02 52.23 61 t a a g c c 36.06 47.54 67.21 0 0 4.918 45.90 4.918 11.47 40.98 6.557 3.278 8.196 27.86 18.03 59.01 3.278 1.639 9.836 19.67 3.278 0 90.16 90.16 54 a c a a g g 53.70 22.22 96.29 94.44 9.259 3.703 9.259 3.703 0 3.703 0 14.81 20.37 27.77 0 0 87.03 68.51 16.66 46.29 3.703 1.851 3.703 12.96 52 t t a g c c 26.92 0 84.61 28.84 0 5.769 34.61 86.53 0 0 1.923 1.923 3.846 11.53 1.923 67.30 0 1.923 34.61 1.923 13.46 3.846 98.07 90.38 47 g c a a t c 6.382 36.17 68.08 93.61 0 0 2.127 0 6.382 4.255 51.06 6.382 89.36 23.40 6.382 2.127 23.40 14.89 2.127 40.42 19.14 0 25.53 78.72 46 g t g g c c 2.173 0 26.08 0 2.173 19.56 30.43 100 2.173 2.173 10.86 6.521 58.69 0 71.73 95.65 0 21.73 8.695 0 0 2.173 86.95 52.17 43 c t a a c a 0 2.325 97.67 95.34 0 58.13 6.976 81.39 0 0 13.95 0 16.27 6.976 0 4.651 32.55 2.325 76.74 9.302 2.325 0 53.48 39.53 43 g t a a t c 0 20.93 67.44 95.34 32.55 0 0 37.20 0 4.651 39.53 0 100 11.62 4.651 0 0 0 0 30.23 27.90 0 27.90 100 42 a a a g t c 92.85 42.85 97.61 7.142 0 19.04 7.142 4.761 2.380 2.380 97.61 11.90 0 40.47 0 90.47 0 7.142 0 11.90 0 0 2.380 61.90 42 t c t t g a 9.523 2.380 33.33 0 0 95.23 78.57 4.761 57.14 97.61 26.19 0 11.90 4.761 9.523 0 40.47 2.380 0 88.09 0 2.380 33.33 2.380 40 t t a g c c 15 0 42.5 0 0 15 80 95 27.5 7.5 0 30 5 0 17.5 92.5 0 0 0 5 12.5 0 100 55 36 g g a g a ag 19.44 5.555 72.22 2.777 100 44.44 13.88 5.555 13.88 0 0 0 55.55 86.11 0 97.22 0 44.44 11.11 2.777 13.88 0 0 11.11 35 c t c t c t 0 0 2.857 14.28 0 40 42.85 100 5.714 71.42 14.28 42.85 2.857 0 2.857 8.571 25.71 5.714 54.28 0 88.57 5.714 60 11.42 35 a a t c a a 71.42 45.71 25.71 5.714 100 97.14 8.571 34.28 57.14 0 0 2.857 8.571 14.28 0 0 0 0 11.42 5.714 17.14 94.28 0 0 34 t t c t c c 8.823 0 0 32.35 0 20.58 91.17 88.23 0 50 47.05 23.52 0 11.76 5.882 5.882 0 2.941 0 0 94.11 11.76 52.94 52.94 32 a c g g a g 56.25 6.25 6.25 3.125 65.62 9.375 0 12.5 0 0 3.125 3.125 43.75 0 93.75 96.87 0 84.37 0 81.25 0 0 31.25 3.125 32 a a t c a a 75 46.87 15.62 0 100 81.25 3.125 25 78.12 0 0 15.62 12.5 25 0 0 0 0 9.375 3.125 6.25 100 0 3.125 28 t g a t c g 0 0 96.42 0 3.571 32.14 92.85 0 0 92.85 3.571 3.571 3.571 96.42 3.571 3.571 0 46.42 3.571 3.571 0 3.571 92.85 17.85 28 g c t g t a 0 3.571 7.142 0 0 53.57 0 3.571 89.28 32.14 78.57 0 100 42.85 0 53.57 7.142 25 0 50 3.571 14.28 14.28 21.42 27 c g g a a a 3.703 14.81 3.703 100 96.29 59.25 33.33 0 0 0 0 0 25.92 85.18 96.29 0 0 40.74 37.03 0 0 0 3.703 0 26 c c g a c c 0 30.76 23.07 100 11.53 0 3.846 0 23.07 0 15.38 0 0 26.92 42.30 0 7.692 3.846 96.15 42.30 11.53 0 65.38 96.15 26 g t g a a g 0 3.846 3.846 46.15 61.53 0 0 80.76 0 7.692 0 0 92.30 15.38 96.15 38.46 19.23 100 7.692 0 0 7.692 19.23 0 25 t c a g a g 16 4 68 36 92 4 68 4 0 0 8 4 16 0 0 64 0 88 0 92 32 0 0 4 25 g g t g t a 0 4 0 0 0 52 0 4 100 24 76 0 100 48 0 60 8 24 0 44 0 16 16 24 25 g g a a g g 20 0 96 44 16 0 32 0 4 12 0 4 48 100 0 36 84 68 0 0 0 8 0 28 24 c t c g a c 20.83 0 4.166 0 83.33 0 0 100 0 0 12.5 37.5 37.5 0 0 100 0 16.66 41.66 0 95.83 0 4.166 45.83

TABLE 2 Emerging gene clusters which were identified by the clustering algorithm pertaining Homo Sapien. The below clusters are arranged according to declining size. For each cluster, the table depicts the distribution of nucleotides for each position along the context sequence. Size of Cluster Distribution of nucleotides per position along the context sequence (%) (number of context sequences)) Function attributes set (Enrichment score/P_value/Benjamini) Pos: −9 −8 −7 −6 −5 −4 −3 −2 −1 1562 cytoskeleton (4.63, 3.7E−7, 2.3E−4); transport (4.63, 9.3E−9, 2.5E−6); g c c g c c a c c transporter activity (4.63, 8.4E−3, 6.2E−1); keratin (4.05, 3.0E−8, 5.9E−6); A % 7.746 3.457 16.00 5.121 6.978 4.737 65.55 3.329 1.728 intermediate filament cytoskeleton (4.05, 7.6E−7, 2.4E−4); T % 7.554 3.072 14.78 4.929 7.746 2.240 2.176 6.402 0.896 Keratin, high sulfur B2 protein (4.05, 2.5E−6, 1.3E−2); G % 67.22 20.42 22.79 72.79 33.73 8.130 29.89 10.94 3.713 negative regulation of physiological process (3.46, 8.2E−5, 5.4E−2); C % 17.47 73.04 46.41 17.15 51.53 84.89 2.368 79.32 93.66 regulation of apoptosis (2.91, 9.7E−5, 4.6E−2); positive regulation of apoptosis (2.91, 8.3E−4, 1.3E−1); apoptosis (2.91, 1.2E−3, 1.6E−1); developmental protein (2.76, 1.3E−4, 1.3E−2); differentiation (2.76, 1.2E−3, 4.8E−2); anti-apoptosis (2.71, 1.7E−2, 5.7E−1); golgi stack (2.61, 4.5E−4, 2.5E−2); Golgi apparatus (2.61, 2.2E−3, 9.2E−2); cellular localization (2.46, 2.4E−3, 2.1E−1); cell organization and biogenesis (2.46, 1.2E−2, 5.0E−1); actin binding (2.44, 5.8E−4, 1.7E−1); organ morphogenesis (2.23, 6.3E−4, 1.1E−1); SF002014:carcinoembryonic antigen (1.97, 7.1E−7, 1.8E−3); pregnancy (1.97, 1.2E−4, 3.8E−2); reproduction (1.97, 1.2E−4, 3.5E−2); pregnancy (1.97, 3.3E−4, 1.9E−2); domain: Ig-like V-type (1.97, 1.0E−3, 1.2E−1); magnesium (1.93, 1.9E−3, 6.5E−2); phosphoric monoester hydrolase activity (1.91, 7.8E−6, 9.7E−3); dephosphorylation (1.91, 3.9E−3, 2.9E−1); protein phosphatase type 1 activity (1.91, 1.0E−2, 6.7E−1); calcium-dependent protein serine/threonine phosphatase activity (1.91, 1.1E−2, 6.9E−1); kinase (1.82, 1.6E−4, 1.5E−2); atp-binding (1.82, 2.6E−4, 1.9E−2); transferase (1.82, 6.4E−4, 3.0E−2); purine nucleotide binding (1.82, 1.0E−3, 2.5E−1); phosphorus metabolism (1.82, 9.4E−3, 4.5E−1); adenyl nucleotide binding (1.82, 1.1E−2, 6.8E−1); ATP (1.82, 4.9E−2, 5.4E−1); myosin (1.58, 6.5E−2, 5.6E−1); reproduction (1.58, 1.2E−4, 3.5E−2); spermatogenesis (1.58, 8.8E−2, 6.7E−1); protein modification (1.58, 1.3E−2, 5.2E−1); oxidoreductase activity, acting on the CH—NH2 group of donors (1.49, 3.3E−3, 4.2E−1); domain: Ubiquitin-like (1.46, 5.7E−3, 4.8E−1); cell-matrix junction (1.33, 3.5E−2, 4.5E−1); focal adhesion (1.33, 1.1E−1, 6.9E−1); microtubule (1.3, 1.3E−3, 5.0E−2); microtubule (1.3, 5.6E−3, 1.7E−1); muscle protein (1.26, 2.0E−3, 6.8E−2); myofibril (1.26, 4.2E−2, 4.6E−1); sarcomere (1.26, 1.0E−1, 6.9E−1); selenium (1.21, 6.1E−2, 5.9E−1); protease (1.2, 2.5E−3, 7.9E−2); serine protease (1.2, 2.7E−3, 8.1E−2); initiation factor (1.15, 7.4E−2, 6.3E−1); cholesterol metabolism (1.15, 1.3E−2, 5.2E−1); steroid metabolism (1.15, 5.1E−2, 5.5E−1); lipid metabolism (1.15, 5.4E−2, 5.6E−1); guanine-nucleotide releasing factor (1.14, 4.9E−3, 1.2E−1); ruffle (1.13, 3.9E−2, 4.6E−1); cell projection (1.13, 6.9E−2, 5.7E−1); vitamin a (1.08, 6.7E−2, 6.1E−1); secretory pathway (1.06, 2.0E−2, 6.3E−1); iron (1.04, 5.5E−4, 2.8E−2); 987 transport (3.62, 1.8E−6, 4.9E−4); lysosome (3.03, 1.3E−4, c c c g g a g c c 1.7E−2); lytic vacuole (3.03, 2.7E−3, 3.4E−1); intracellular A % 5.065 2.735 17.93 14.89 3.850 39.31 1.722 4.863 1.013 signaling cascade (2.61, 5.2E−4, 3.6E−1); KRAB-related T % 10.23 10.63 21.07 8.510 7.700 9.827 0.607 5.065 1.013 (2.49, 1.2E−7, 6.1E−4); ion transport (1.79, 7.3E−4, 7.3E−2); G % 26.74 26.54 25.63 47.01 46.80 17.62 95.84 3.444 4.964 potassium (1.79, 2.3E−3, 1.6E−1); voltage-gated C % 57.95 60.08 35.35 29.58 41.64 33.23 1.823 86.62 93.00 channel (1.79, 2.2E−2, 5.0E−1); differentiation (1.65, 1.1E−2, 4.0E−1); ATPase activity, coupled to transmembrane movement of substances (1.6, 1.4E−3, 6.9E−1); hydrolase activity, acting on acid anhydrides, catalyzing transmembrane movement of substances (1.6, 2.4E−3, 6.9E−1); atp synthesis (1.6, 6.0E−3, 2.9E−1); hydrogen ion transport (1.6, 9.6E−3, 3.7E−1); growth regulation (1.59, 7.1E−3, 3.1E−1); actin-binding (1.43, 6.9E−3, 3.1E−1); lipoprotein (1.39, 1.6E−2, 4.6E−1); wnt signaling pathway (1.23, 1.7E−2, 4.6E−1); cell cycle (1.17, 4.8E−3, 2.5E−1); nucleotide-binding (1.14, 7.2E−6, 1.4E−3); atp-binding (1.14, 2.3E−4, 2.8E−2); transferase (1.14, 1.1E−3, 9.7E−2); prenylation (0.98, 4.9E−2, 6.9E−1); growth factor (0.94, 2.7E−2, 5.5E−1); thick filament (0.91, 2.0E−2, 5.1E−1); muscle protein (0.91, 3.1E−2, 5.9E−1); methylation (0.91, 4.2E−2, 6.5E−1); golgi stack (0.9, 2.6E−2, 5.4E−1); mitochondrion (0.86, 5.0E−2, 6.9E−1); cell cycle (0.84, 4.8E−3, 2.5E−1); cell division (0.84, 2.2E−2, 5.1E−1); redox-active center (0.82, 3.3E−2, 6.1E−1); chaperone (0.75, 5.2E−2, 6.9E−1); lipid synthesis (0.73, 1.6E−2, 4.6E−1); protein phosphatase inhibitor (0.68, 5.6E−2, 6.9E−1); aminoacyltransferase (0.62, 5.6E−2, 6.9E−1); immune response (0.31, 3.5E−2, 6.0E−1); nuclear protein (0.29, 4.7E−2, 6.8E−1); nuclear protein (0.22, 4.7E−2, 6.8E−1); 407 response to pest, pathogen or parasite (2.79, 4.5E−5, 1.4E−1); g c c a g c a c c response to wounding (2.79, 8.1E−5, 1.3E−1); response A % 26.53 10.31 5.651 75.92 3.194 1.474 92.62 7.371 3.931 to other organism (2.79, 1.1E−4, 1.2E−1); response to T % 6.879 13.02 3.439 12.28 2.702 0.737 0.491 5.896 0.982 stress (2.79, 2.0E−4, 1.5E−1); ANTIGEN PROCESSING G % 38.82 36.11 14.49 7.371 92.62 24.81 5.159 19.16 7.862 AND PRESENTATION (2.36, 1.2E−3, 2.1E−1); C % 27.76 40.54 76.41 4.422 1.474 72.97 1.719 67.56 87.22 immunoglobulin domain (2.36, 3.7E−3, 3.5E−1); signal (2.17, 6.0E−6, 8.2E−3); transmembrane (2.17, 1.0E−3, 2.1E−1); glycoprotein (2.17, 1.1E−3, 1.9E−1); Glutathione S-transferase, Mu class (1.81, 3.4E−5, 1.6E−1); nucleotide phosphate-binding region: PAPS (1.57, 8.6E−5, 2.9E−1); aryl sulfotransferase activity (1.57, 6.2E−4, 5.4E−1); sulfotransferase activity (1.57, 9.2E−4, 5.4E−1); transferase activity, transferring sulfur-containing groups (1.57, 1.4E−3, 5.9E−1); catecholamine metabolism (1.57, 2.2E−3, 2.9E−1); sulfotransferase (1.57, 6.5E−3, 4.9E−1); lipid metabolism (1.57, 8.3E−3, 5.1E−1); intermediate filament (1.47, 2.4E−3, 2.8E−1); membrane (1.35, 7.8E−5, 2.6E−2); transmembrane (1.35, 1.0E−3, 2.1E−1); developmental protein (1.11, 8.8E−3, 5.1E−1); transcription factor (0.85, 7.3E−3, 4.9E−1); transferase (0.5, 1.9E−3, 2.8E−1); 398 locomotion (3.03, 9.3E−4, 6.5E−1); localization of cell g c c g c c a a g (3.03, 9.3E−4, 6.5E−1); cell motility (3.03, 9.3E−4, 6.5E−1); A % 5.527 5.276 7.286 2.512 5.778 2.763 93.96 75.87 3.015 ribonucleoprotein (1.56, 2.0E−4, 8.5E−2); ribosome T % 3.266 13.56 13.06 18.34 9.547 2.010 0 0 1.005 (1.56, 9.5E−3, 6.6E−1); RNA binding (1.47, 2.2E−4, 4.2E−1); G % 73.61 21.10 25.37 69.59 35.17 16.83 2.010 21.10 87.68 rna-binding (1.47, 6.4E−4, 2.0E−1); cell cycle (0.91, C % 17.58 60.05 54.27 9.547 49.49 78.39 4.020 3.015 8.291 5.5E−3, 5.6E−1); cell division (0.91, 1.1E−2, 6.7E−1); sodium/potassium transport (0.81, 3.6E−3, 5.6E−1); potassium transport (0.81, 7.7E−3, 6.5E−1); potassium (0.81, 1.1E−2, 6.9E−1); 368 isomerase (1.4, 3.4E−3, 6.9E−1); g g c c c c g c c A % 13.31 2.445 1.902 3.532 1.086 27.71 1.630 2.989 2.445 T % 5.706 13.58 3.260 10.59 12.5 2.173 0.271 2.173 1.630 G % 55.97 77.98 17.66 16.84 6.793 20.38 96.73 1.630 4.891 C % 25 5.978 77.17 69.02 79.61 49.72 1.358 93.20 91.03 347 hormone (2.01, 2.1E−3, 4.4E−1); transport (1.83, 7.4E−4, t c c t c c a g g 2.2E−1); signal (1.79, 5.9E−6, 4.0E−3); lipid transport A % 30.54 2.305 6.628 16.71 4.034 6.628 92.21 30.54 3.746 (0.97, 2.2E−3, 3.9E−1); nuclear protein (0.86, 4.6E−3, T % 32.27 11.81 11.23 36.59 18.73 4.034 2.017 0.864 1.440 5.9E−1); ubl conjugation pathway (0.79, 5.5E−3, 5.6E−1); G % 20.74 36.88 2.305 12.39 32.85 8.357 2.593 66.85 78.67 nuclear protein (0.64, 4.6E−3, 5.9E−1); membrane (0.57, 5.0E−3, 5.8E−1); C % 16.42 48.99 79.82 34.29 44.38 80.97 3.170 1.729 16.13 245 membrane (0.88, 2.2E−3, 5.3E−1); g c c g a g g c c A % 2.040 4.081 11.42 8.979 35.91 0.816 10.61 1.632 8.979 T % 1.632 2.448 10.20 4.489 6.938 1.224 2.448 0 2.857 G % 58.36 1.632 20.40 62.04 23.26 97.95 84.08 15.10 27.34 C % 37.95 91.83 57.95 24.48 33.87 0 2.857 83.26 60.81 196 transcription cofactor activity (1.23, 3.9E−4, 6.2E−1); c g c g g g a a g transcription cofactor activity (1.07, 3.9E−4, 6.2E−1); A % 28.57 5.612 17.85 35.71 28.06 1.530 97.44 68.87 2.040 signal-anchor (0.99, 2.1E−3, 6.1E−1); T % 15.30 2.551 3.571 2.040 2.040 0.510 0 0.510 1.020 G % 22.95 88.26 33.67 57.65 67.34 97.44 2.040 30.10 94.89 C % 33.16 3.571 44.89 4.591 2.551 0.510 0.510 0.510 2.040 177 defensin (2.34, 2.7E−4, 3.1E−1); SF001875: mammalian t c c c c a g c c defensin (2.34, 3.6E−4, 6.1E−1); Mammalian defensin A % 6.214 12.42 9.039 23.72 2.824 77.40 25.42 2.824 2.259 (2.34, 4.4E−4, 5.3E−1); DEFSN (2.34, 4.5E−4, 2.3E−1); T % 46.89 7.344 10.73 22.59 3.389 6.779 0.564 2.259 2.259 fungicide (2.34, 1.2E−3, 4.2E−1); antibiotic (2.34, 1.6E−3, G % 33.33 4.519 3.389 3.954 0 11.86 74.01 2.824 1.694 4.2E−1); antimicrobial (2.34, 1.7E−3, 3.8E−1); homodimer C % 13.55 75.70 76.83 49.71 93.78 3.954 0 92.09 93.78 (2.34, 2.3E−3, 4.0E−1); 175 zinc ion binding (1.72, 1.5E−5, 3.8E−2); transition metal g a g g a g a a g ion binding (1.72, 6.4E−5, 7.8E−2); zinc (1.72, 7.1E−4, A % 7.428 66.85 18.85 4.571 68.57 8 52.57 95.42 12.57 6.2E−1); nuclear protein (1.72, 9.1E−4, 4.6E−1); zinc- T % 6.285 9.142 16.57 1.714 28 2.285 0.571 1.142 1.142 finger (1.72, 1.4E−3, 4.8E−1); G % 50.85 4 61.71 74.85 1.142 85.14 44 2.857 84 C % 35.42 20 2.857 18.85 2.285 4.571 2.857 0.571 2.285 165 membrane (1.34, 6.2E−4, 3.4E−1); g a g g g a g c c A % 16.96 53.33 16.96 1.818 17.57 92.12 30.90 26.06 1.212 T % 9.090 18.78 1.212 3.636 4.848 3.636 0 0.606 1.212 G % 41.81 18.78 78.18 92.12 44.24 3.636 67.27 21.21 3.030 C % 32.12 9.090 3.636 2.424 33.33 0.606 1.818 52.12 94.54 161 Glycoside hydrolase family 13 (2.56, 7.0E−5, 3.0E−1); g a a a g c a a a Alpha amylase, all-beta (2.56, 7.0E−5, 3.0E−1); Aamy_C A % 12.42 73.91 93.78 41.61 21.73 38.50 97.51 93.78 66.45 (2.56, 9.2E−5, 5.2E−2); SF500178: alpha-amylase, short T % 4.968 0 0.621 14.90 33.54 6.211 0 1.242 1.242 form (2.56, 1.2E−4, 2.6E−1); SF001019: alpha-amylase G % 76.39 25.46 3.726 16.77 39.13 13.66 1.242 1.242 29.81 (2.56, 1.2E−4, 2.6E−1); binding site: Chloride (2.56, 3.0E−4, C % 6.211 0.621 1.863 26.70 5.590 41.61 1.242 3.726 2.484 6.9E−1); Alpha amylase, catalytic region (2.56, 3.5E−4, 4.5E−1); Alpha amylase, catalytic subdomain (2.56, 3.5E−4, 4.5E−1); amylase activity (2.56, 4.3E−4, 6.6E−1); alpha- amylase activity (2.56, 4.3E−4, 6.6E−1); Aamy (2.56, 4.6E−4, 1.2E−1); 106 nucleotide binding (3.76, 3.2E−5, 7.6E−2); nucleotide- c t t t c c a a a binding (3.76, 3.8E−5, 5.1E−2); ATP binding (3.76, 6.4E−5, A % 16.03 2.830 5.660 0.943 1.886 5.660 83.96 86.79 66.03 7.7E−2); adenyl nucleotide binding (3.76, 9.8E−5, 7.9E−2); T % 5.660 54.71 92.45 62.26 41.50 1.886 1.886 2.830 2.830 purine nucleotide binding (3.76, 1.5E−4, 8.8E−2); atp- G % 29.24 0.943 0 27.35 3.773 2.830 3.773 4.716 30.18 binding (3.76, 1.5E−4, 9.8E−2); C % 49.05 41.50 1.886 9.433 52.83 89.62 10.37 5.660 0.943 104 keratin (4.63, 2.9E−8, 4.0E−5); repeat: 6 (4.63, 5.7E−8, g a c a g a a c c 2.3E−4); repeat: 5 (4.63, 1.5E−7, 2.9E−4); repeat: 1 (4.63, A % 39.42 72.11 3.846 98.07 3.846 69.23 88.46 3.846 18.26 2.6E−7, 3.5E−4); repeat: 2 (4.63, 2.7E−7, 2.7E−4); repeat: 4 T % 2.884 0.961 1.923 1.923 5.769 2.884 1.923 8.653 8.653 (4.63, 3.4E−7, 2.7E−4); intermediate filament cytoskeleton G % 41.34 6.730 43.26 0 88.46 10.57 1.923 7.692 14.42 (4.63, 9.3E−7, 5.8E−4); intermediate filament (4.63, 9.3E−7, C % 16.34 20.19 50.96 0 1.923 17.30 7.692 79.80 58.65 5.8E−4); repeat: 3 (4.63, 1.0E−6, 6.8E−4); repeat: 7 (4.63, 9.8E−6, 5.6E−3); repeat: 8 (4.63, 1.1E−3, 4.3E−1); SF000050: human cytochrome P450 CYP4B1 (2.01, 9.6E−6, 2.4E−2); E-class P450, group I (2.01, 5.5E−5, 2.5E−1); electron transport (2.01, 1.2E−4, 3.3E−1); generation of precursor metabolites and energy (2.01, 2.0E−4, 2.9E−1); Cytochrome P450 (2.01, 2.3E−4, 4.5E−1); monooxygenase (2.01, 3.4E−4, 2.1E−1); tetrapyrrole binding (2.01, 8.7E−4, 5.2E−1); heme binding (2.01, 8.7E−4, 5.2E−1); heme (2.01, 1.2E−3, 4.2E−1); monooxygenase activity (2.01, 1.2E−3, 5.3E−1); SCP (1.91, 1.6E−3, 6.1E−1); 104 structural protein (1.38, 3.6E−4, 3.9E−1); cytoskeleton a a g a a g a a a (1.38, 1.5E−3, 6.5E−1); A % 92.30 80.76 33.65 76.92 72.11 19.23 98.07 88.46 61.53 T % 1.923 3.846 0.961 1.923 6.730 9.615 0 1.923 3.846 G % 1.923 12.5 60.57 1.923 17.30 61.53 1.923 0.961 14.42 C % 3.846 2.884 4.807 19.23 3.846 9.615 0 8.653 20.19 101 organelle envelope (2.43, 1.4E−3, 5.8E−1); envelope c a a g c c a a g (2.43, 1.6E−3, 3.8E−1); nuclear envelope (2.43, 7.0E−3, A % 3.960 51.48 88.11 24.75 3.960 0 93.06 92.07 15.84 5.8E−1); endomembrane system (2.43, 1.3E−2, 6.8E−1); T % 15.84 6.930 2.970 0.990 9.900 2.970 1.980 0.990 0.990 cytoplasm (1.34, 3.2E−3, 4.8E−1); intracellular (1.34, G % 33.66 10.89 4.950 70.29 8.910 0.990 3.960 3.960 73.26 5.0E−3, 5.4E−1); electron transport (1.13, 5.3E−4, 3.0E−1); C % 46.53 30.69 3.960 3.960 77.22 96.03 0.990 2.970 9.900 OXIDATIVE PHOSPHORYLATION (1.13, 4.9E−3, 6.2E−1); 82 transmembrane (1.68, 1.6E−3, 6.5E−1); c t g g g g a a c A % 1.219 0 0 7.317 4.878 20.73 80.48 54.87 40.24 T % 24.39 97.56 8.536 3.658 3.658 6.097 13.41 0 2.439 G % 18.29 2.439 80.48 86.58 85.36 62.19 3.658 20.73 8.536 C % 56.09 0 10.97 2.439 6.097 10.97 2.439 24.39 48.78 76 SF001638: cystatin (2.05, 1.4E−4, 3.1E−1); site: Reactive c g g g a g a c c site (2.05, 2.6E−4, 6.5E−1); thiol protease inhibitor (2.05, A % 13.15 40.78 3.947 1.315 89.47 3.947 61.84 17.10 1.315 8.4E−4, 6.8E−1); CY (2.05, 1.4E−3, 5.6E−1); T % 3.947 11.84 5.263 3.947 6.578 2.631 1.315 26.31 2.631 G % 35.52 46.05 89.47 90.78 3.947 93.42 30.26 26.31 14.47 C % 47.36 1.315 1.315 3.947 0 0 6.578 30.26 81.57 75 Keratin, high sulfur B2 protein (3.3, 5.8E−6, 3.0E−2); c a c c g c a g c repeat: 18 (3.3, 9.1E−6, 3.6E−2); keratin filament (3.3, A % 0 54.66 1.333 1.333 17.33 2.666 90.66 14.66 8 1.1E−5, 6.5E−3); intermediate filament cytoskeleton (3.3, T % 2.666 9.333 4 5.333 22.66 0 4 4 5.333 2.4E−4, 4.9E−2); intermediate filament (3.3, 2.4E−4, 4.9E−2); G % 1.333 6.666 26.66 4 34.66 2.666 1.333 60 2.666 plasma membrane (0.85, 6.9E−3, 6.6E−1); C % 96 29.33 68 89.33 25.33 94.66 4 21.33 84 72 tumor antigen (6.54, 9.0E−14, 1.2E−10); domain: MAGE a g a g t c a t c (6.54, 3.5E−11, 1.4E−7); SF005491: tumor associated A % 68.05 0 50 16.66 2.777 0 97.22 34.72 4.166 protein MAGE (6.54, 3.9E−11, 1.0E−7); MAGE protein T % 2.777 6.944 1.388 34.72 73.61 0 0 63.88 0 (6.54, 2.1E−10, 1.1E−6); multigene family (6.54, 3.9E−7, G % 26.38 90.27 11.11 48.61 16.66 9.722 2.777 1.388 0 2.6E−4); antigen (6.54, 8.1E−6, 3.7E−3); C % 2.777 2.777 37.5 0 6.944 90.27 0 0 95.83 72 nuclear protein (2.71, 3.6E−8, 4.9E−5); transcription (2.71, g g a g g a a a a 2.7E−6, 1.8E−3); transcription regulation (2.71, 3.4E−6, A % 44.44 1.388 47.22 13.88 22.22 93.05 100 88.88 56.94 1.5E−3); KRAB box (2.71, 4.4E−5, 2.0E−1); zinc finger T % 2.777 1.388 11.11 2.777 1.388 0 0 1.388 0 region: C2H2-type 9 (2.71, 8.6E−5, 2.9E−1); KRAB (2.71, G % 52.77 95.83 33.33 83.33 58.33 1.388 0 4.166 37.5 9.0E−5, 5.1E−2); dna-binding (2.71, 1.0E−4, 3.4E−2); C % 0 1.388 8.333 0 18.05 5.555 0 5.555 5.555 intracellular membrane-bound organelle (2.71, 7.6E−4, 2.1E−1); metal-binding (2.71, 1.2E−3, 2.7E−1); regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolism (2.71, 1.3E−3, 5.8E−1); 71 SF002282: cytoskeletal keratin (1.84, 1.3E−4, 2.9E−1); c t t g g a a c c intermediate filament (1.84, 3.3E−4, 3.6E−1); keratin A % 40.84 0 5.633 19.71 0 49.29 91.54 1.408 0 (1.84, 2.2E−3, 6.3E−1); T % 0 91.54 76.05 8.450 1.408 5.633 1.408 11.26 0 G % 0 1.408 1.408 71.83 67.60 4.225 7.042 1.408 0 C % 59.15 7.042 16.90 0 30.98 40.84 0 85.91 100 69 KRAB box (3.66, 2.1E−10, 1.1E−6); Zinc finger, C2H2- c t g a a g a a a subtype (3.66, 1.4E−9, 3.6E−6); KRAB (3.66, 3.4E−9, A % 1.449 27.53 14.49 50.72 98.55 28.98 89.85 84.05 81.15 2.0E−6); zinc (3.66, 6.7E−9, 9.1E−6); Zinc finger, C2H2- T % 23.18 72.46 1.449 5.797 0 0 1.449 5.797 7.246 type (3.66, 8.8E−9, 1.5E−5); zinc-finger (3.66, 1.1E−8, G % 2.898 0 76.81 26.08 0 66.66 4.347 2.898 10.14 7.6E−6); transcription (3.66, 7.1E−8, 3.2E−5); transcription C % 72.46 0 7.246 17.39 1.449 4.347 4.347 7.246 1.449 regulation (3.66, 9.5E−8, 3.2E−5); zinc ion binding (3.66, 1.4E−7, 3.5E−4); transition metal ion binding (3.66, 1.5E−7, 1.8E−4); ZnF_C2H2 (3.66, 1.6E−7, 4.6E−5); metal- binding (3.66, 2.9E−7, 7.9E−5); nuclear protein (3.66, 2.6E−6, 6.0E−4); cation binding (3.66, 3.5E−6, 2.9E−3); zinc finger region: C2H2-type 8 (3.66, 1.1E−5, 4.2E−2); regulation of transcription (3.66, 1.2E−5, 4.1E−2); metal ion binding (3.66, 1.2E−5, 6.3E−3); ion binding (3.66, 1.2E−5, 6.3E−3); regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolism (3.66, 1.5E−5, 2.5E−2); regulation of transcription, DNA-dependent (3.66, 2.0E−5, 2.2E−2); zinc finger region: C2H2-type 7 (3.66, 2.1E−5, 4.2E−2); domain: KRAB (3.66, 2.2E−5, 2.9E−2); transcription (3.66, 2.4E−5, 2.0E−2); transcription, DNA-dependent (3.66, 3.0E−5, 2.0E−2); regulation of cellular metabolism (3.66, 3.4E−5, 1.9E−2); zinc finger region: C2H2-type 6 (3.66, 3.5E−5, 3.5E−2); General function prediction only (3.66, 4.4E−5, 3.0E−3); regulation of metabolism (3.66, 5.0E−5, 2.4E−2); zinc finger region: C2H2-type 5 (3.66, 8.1E−5, 6.3E−2); zinc finger region: C2H2-type 4 (3.66, 1.3E−4, 8.6E−2); zinc finger region: C2H2-type 1 (3.66, 1.6E−4, 8.6E−2); nucleic acid binding (3.66, 1.9E−4, 7.5E−2); zinc finger region: C2H2-type 2 (3.66, 2.5E−4, 1.2E−1); zinc finger region: C2H2-type 3 (3.66, 2.9E−4, 1.2E−1); nucleobase, nucleoside, nucleotide and nucleic acid metabolism (3.66, 5.5E−4, 2.1E−1); regulation of cellular physiological process (3.66, 7.0E−4, 2.3E−1); regulation of physiological process (3.66, 1.0E−3, 3.0E−1); regulation of biological process (3.66, 1.1E−3, 3.0E−1); regulation of cellular process (3.66, 1.3E−3, 3.1E−1); dna-binding (3.66, 4.5E−3, 5.9E−1); 67 signal (2.42, 9.0E−5, 1.2E−1); glycoprotein (2.42, 5.7E−4, c t g c c c a g c 3.2E−1); sushi (2.17, 3.3E−3, 6.8E−1); domain: Ig-like C2- A % 5.970 0 20.89 2.985 26.86 19.40 94.02 4.477 7.462 type 3 (1.73, 1.9E−4, 5.3E−1); cell adhesion (1.73, 5.0E−3, T % 2.985 70.14 0 23.88 4.477 5.970 2.985 0 2.985 6.8E−1); transcription regulation (1.27, 1.5E−3, 4.8E−1); G % 2.985 28.35 76.11 31.34 0 11.94 2.985 95.52 10.44 C % 88.05 1.492 2.985 41.79 68.65 62.68 0 0 79.10 63 GLUTATHIONE METABOLISM (1.93, 5.5E−3, 6.6E−1); a c t g c a a t c A % 87.30 3.174 7.936 1.587 20.63 82.53 74.60 4.761 1.587 T % 0 6.349 84.12 0 6.349 11.11 3.174 47.61 0 G % 6.349 7.936 3.174 98.41 12.69 0 20.63 20.63 3.174 C % 6.349 82.53 4.761 0 60.31 6.349 1.587 26.98 95.23 60 cellular metabolism (1.98, 1.3E−5, 4.4E−2); metabolism c c c g c c g c g (1.98, 7.5E−5, 1.2E−1); primary metabolism (1.98, 8.6E−4, A % 1.666 1.666 3.333 31.66 1.666 3.333 1.666 0 3.333 6.2E−1); cellular physiological process (1.98, 9.7E−4, T % 1.666 36.66 0 5 0 1.666 3.333 0 0 5.6E−1); intracellular membrane-bound organelle (1.98, G % 0 6.666 3.333 63.33 33.33 0 90 5 96.66 1.9E−3, 6.9E−1); membrane-bound organelle (1.98, 1.9E−3, C % 96.66 55 93.33 0 65 95 5 95 0 4.4E−1); regulation of cellular process (1.98, 2.4E−3, 6.9E−1); cytoplasm (1.98, 4.7E−3, 6.3E−1); intracellular (1.98, 5.8E−3, 6.0E−1); intracellular organelle (1.98, 8.4E−3, 6.5E−1); organelle (1.98, 8.5E−3, 5.9E−1); 50 domain: KRAB (13.16, 2.7E−29, 1.1E−25); KRAB box a g c c t a g a a (13.16, 1.9E−27, 1.0E−23); KRAB (13.16, 1.9E−24, 1.1E−21); A % 100 6 4 8 28 68 0 96 82 zinc finger region: C2H2-type 10 (13.16, 2.7E−24, T % 0 10 16 0 44 0 0 0 2 5.4E−21); Zinc finger, C2H2-subtype (13.16, 3.7E−24, G % 0 84 2 8 28 30 100 4 0 9.6E−21); zinc finger region: C2H2-type 7 (13.16, 8.0E−24, C % 0 0 78 84 0 2 0 0 16 1.1E−20); transcription (13.16, 3.4E−19, 4.6E−16); transcription regulation (13.16, 5.4E−19, 3.6E−16); zinc finger region: C2H2-type 3 (13.16, 8.9E−19, 3.2E−16); zinc finger region: C2H2-type 5 (13.16, 9.8E−19, 3.3E−16); dna-binding (13.16, 1.3E−17, 4.6E−15); regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolism (13.16, 7.1E−14, 6.0E−11); regulation of cellular metabolism (13.16, 2.7E−13, 1.5E−10); regulation of metabolism (13.16, 5.3E−13, 2.3E−10); nucleic acid binding (13.16, 4.8E−10, 3.0E−7); primary metabolism (13.16, 1.6E−5, 4.2E−3); intracellular membrane-bound organelle (13.16, 3.5E−5, 1.1E−2); zinc finger region: C2H2-type 14 (7.98, 3.1E−16, 9.5E−14); zinc finger region: C2H2-type 15 (7.98, 2.3E−13, 6.3E−11); zinc finger region: C2H2-type 16 (7.98, 1.1E−8, 2.7E−6); zinc finger region: C2H2-type 17 (7.98, 5.3E−6, 1.1E−3); zinc finger region: C2H2-type 18 (7.98, 1.1E−4, 2.1E−2); zinc finger region: C2H2-type 19 (7.98, 2.7E−3, 3.9E−1); SF005559: zinc finger protein ZFP-36 (3.77, 9.7E−7, 2.5E−3); 47 B melanoma antigen (2.56, 3.3E−8, 1.7E−4); t g c a g c a a g A % 46.80 25.53 34.04 87.23 4.255 2.127 93.61 100 0 T % 48.93 6.382 0 0 4.255 25.53 0 0 2.127 G % 2.127 65.95 0 8.510 87.23 2.127 4.255 0 95.74 C % 2.127 2.127 65.95 4.255 4.255 70.21 2.127 0 2.127 45 zinc-finger (2.04, 1.0E−4, 1.3E−1); ion binding (2.04, g g c g g a g a c 3.4E−4, 3.5E−1); metal ion binding (2.04, 3.4E−4, 3.5E−1); A % 0 26.66 0 0 22.22 93.33 2.222 95.55 0 zinc (2.04, 5.4E−4, 3.1E−1); cation binding (2.04, 5.8E−4, T % 0 13.33 40 20 4.444 2.222 0 2.222 2.222 3.8E−1); metal-binding (2.04, 8.0E−4, 3.0E−1); zinc ion G % 100 48.88 2.222 77.77 51.11 4.444 97.77 2.222 35.55 binding (2.04, 8.7E−4, 4.2E−1); transition metal ion C % 0 11.11 57.77 2.222 22.22 0 0 0 62.22 binding (2.04, 1.1E−3, 4.3E−1); DNA binding (2.04, 1.6E−3, 4.8E−1); DNA binding (1.33, 1.6E−3, 4.8E−1); DNA binding (0.99, 1.6E−3, 4.8E−1); 44 DNA binding (1.99, 9.4E−4, 6.9E−1); g c c g c c c g g A % 0 0 4.545 9.090 6.818 0 2.272 0 0 T % 0 0 18.18 2.272 0 2.272 9.090 4.545 6.818 G % 90.90 0 2.272 86.36 6.818 29.54 27.27 77.27 93.18 C % 9.090 100 75 2.272 86.36 68.18 61.36 18.18 0 44 Zinc finger, C2H2-subtype (2.11, 2.9E−9, 1.5E−5); g a g g c a g g g ZnF_C2H2 (2.11, 1.4E−6, 8.2E−4); Zinc finger, C2H2- A % 2.272 100 0 4.545 4.545 77.27 45.45 31.81 13.63 type (2.11, 2.5E−6, 6.4E−3); KRAB (2.11, 8.7E−6, 2.5E−3); T % 0 0 0 29.54 0 4.545 0 2.272 4.545 zinc-finger (2.11, 1.1E−5, 1.4E−2); zinc ion binding G % 95.45 0 97.72 47.72 0 18.18 50 54.54 81.81 (2.11, 4.3E−5, 1.0E−1); transition metal ion binding (2.11, C % 2.272 0 2.272 18.18 95.45 0 4.545 11.36 0 5.3E−5, 6.4E−2); zinc (2.11, 6.2E−5, 4.1E−2); metal- binding (2.11, 7.6E−5, 3.4E−2); nucleic acid binding (2.11, 4.5E−4, 3.1E−1); ion binding (2.11, 8.1E−4, 3.3E−1); metal ion binding (2.11, 8.1E−4, 3.3E−1); cation binding (2.11, 1.5E−3, 4.7E−1); transcription (2.11, 2.5E−3, 5.7E−1); transcription regulation (2.11, 2.8E−3, 5.3E−1); nuclear protein (2.11, 4.7E−3, 6.6E−1); Transcription/Cell division and chromosome partitioning (2.11, 5.9E−3, 1.8E−1); 43 domain: MAGE (3.45, 2.3E−5, 8.9E−2); MAGE protein a c a g c c a g c (3.45, 2.4E−5, 1.2E−1); antigen (3.45, 4.5E−4, 4.6E−1); A % 67.44 4.651 100 0 2.325 6.976 100 30.23 2.325 T % 11.62 4.651 0 2.325 2.325 2.325 0 20.93 4.651 G % 0 9.302 0 81.39 4.651 2.325 0 32.55 0 C % 20.93 81.39 0 16.27 90.69 88.37 0 16.27 93.02

TABLE 3 Emerging gene clusters which were identified by the clustering algorithm pertaining Mus Musculus. The below clusters are arranged according to declining size. For each cluster, the table depicts the distribution of nucleotides for each position along the context sequence. Size of Distribution of nucleotides per position along the context sequence (%) Cluster (number of context sequences) Function attributes set (Enrichment score/P_value/Benjamini) Pos: -9 -8 -7 -6 -5 -4 -3 -2 -1 1197 intracellular non-membrane-bound organelle (7.46, 1.0E−9, g c c g c c g c c 1.6E−7); non-membrane-bound organelle (7.46, 1.0E−9, A % 11.44 2.756 27.23 4.594 9.857 36.42 47.11 2.840 1.169 1.6E−7); cytoskeleton (7.46, 1.2E−9, 1.5E−7); organelle T % 8.103 5.430 16.95 6.182 8.020 5.680 0.835 3.675 1.587 organization and biogenesis (7.46, 1.2E−3, 2.2E−1); G % 57.14 14.70 10.02 81.03 25.48 6.683 50.04 2.088 4.010 transport (5.55, 2.2E−11, 6.4E−9); transport (5.55, 4.0E−5, C % 23.30 77.10 45.78 8.187 56.64 51.21 2.005 91.39 93.23 2.4E−2); transporter activity (5.55, 1.1E−3, 1.2E−1); actin cytoskeleton (4.99, 1.1E−7, 8.7E−6); actin-binding (4.99, 1.5E−6, 1.2E−4); cytoskeletal protein binding (4.99, 1.6E−4, 3.6E−2); actin binding (4.99, 3.8E−4, 6.9E−2); protein transport (3.84, 5.5E−7, 5.3E−5); cell organization and biogenesis (3.84, 1.1E−5, 9.6E−3); protein transporter activity (3.84, 4.4E−5, 1.2E−2); protein localization (3.84, 1.1E−4, 5.4E−2); protein transport (3.84, 2.7E−4, 1.2E−1); establishment of protein localization (3.84, 3.7E−4, 1.4E−1); intracellular transport (3.84, 7.7E−4, 1.8E−1); establishment of cellular localization (3.84, 9.0E−4, 1.9E−1); cellular localization (3.84, 1.1E−3, 2.2E−1); intracellular protein transport (3.84, 2.1E−3, 3.1E−1); tissue kallikrein activity (3.41, 1.3E−12, 3.3E−9); serine protease (3.41, 6.5E−6, 4.1E−4); serine proteinase (3.41, 9.7E−6, 5.6E−4); SF001135: trypsin (3.41, 2.0E−5, 4.2E−2); submandibular gland (3.41, 2.5E−5, 1.2E−3); zymogen (3.41, 3.2E−5, 1.5E−3); protease (3.41, 2.5E−4, 8.9E−3); Peptidase S1A, chymotrypsin (3.41, 8.3E−4, 5.7E−1); Peptidase S1 and S6, chymotrypsin/Hap (3.41, 1.2E−3, 5.9E−1); serine-type endopeptidase activity (3.41, 3.5E−3, 2.9E−1); serine-type peptidase activity (3.41, 6.4E−3, 3.9E−1); Tryp_SPc (3.41, 7.0E−3, 6.4E−1); Pleckstrin-like (3.34, 4.3E−4, 4.2E−1); metal-binding (3.32, 6.2E−7, 5.5E−5); muscle protein (2.87, 7.5E−6, 4.5E−4); contractile fiber (2.87, 2.7E−5, 1.6E−3); myofibril (2.87, 9.5E−5, 4.5E−3); sarcomere (2.87, 2.4E−4, 9.7E−3); muscle (2.87, 3.6E−3, 7.5E−2); cellular macromolecule metabolism (2.87, 5.1E−3, 4.7E−1); protein metabolism (2.87, 5.4E−3, 4.8E−1); cellular protein metabolism (2.87, 1.1E−2, 5.7E−1); golgi stack (2.82, 3.7E−5, 1.6E−3); Golgi apparatus (2.82, 7.8E−3, 1.6E−1); Golgi stack (2.82, 1.2E−2, 2.2E−1); basolateral plasma membrane (2.61, 8.0E−5, 4.1E−3); adherens junction (2.61, 3.3E−4, 1.2E−2); cell-substrate adherens junction (2.61, 1.4E−3, 4.5E−2); cell-matrix junction (2.61, 2.2E−3, 5.6E−2); focal adhesion (2.61, 4.7E−3, 1.1E−1); Proteasome component region PCI (2.48, 1.7E−4, 3.6E−1); PINT (2.48, 9.4E−4, 1.7E−1); cell projection organization and biogenesis (2.45, 5.8E−4, 1.7E−1); cell projection biogenesis (2.45, 2.5E−3, 3.3E−1); GTPase regulator activity (2.37, 2.1E−3, 2.0E−1); small GTPase regulator activity (2.37, 4.1E−3, 3.1E−1); enzyme regulator activity (2.37, 8.6E−3, 4.5E−1); enzyme binding (2.33, 1.1E−3, 1.2E−1); kinase binding (2.33, 3.7E−3, 2.9E−1); magnesium (2.23, 4.8E−4, 1.6E−2); magnesium ion binding (2.23, 1.0E−2, 4.7E−1); mRNA metabolism (2.21, 7.0E−4, 1.8E−1); mrna processing (2.21, 7.8E−4, 2.3E−2); mRNA processing (2.21, 1.8E−3, 2.9E−1); mrna splicing (2.21, 2.2E−3, 5.0E−2); alkali metal ion binding (2.19, 5.4E−3, 3.5E−1); monovalent inorganic cation transport (2.19, 5.5E−3, 4.7E−1); cation transport (2.19, 7.9E−3, 5.3E−1); ion transport (2.19, 8.3E−3, 5.4E−1); potassium transport (2.19, 1.2E−2, 1.7E−1); ionic channel (2.19, 2.9E−2, 3.2E−1); Arf GTPase activating protein (2.14, 9.4E−5, 3.8E−1); ArfGap (2.14, 1.2E−4, 6.9E−2); carbohydrate metabolism (2.09, 5.6E−4, 1.8E−2); glycogen metabolism (2.09, 1.8E−3, 4.5E−2); glucan metabolism (2.09, 1.4E−2, 6.3E−1); glycogen metabolism (2.09, 1.4E−2, 6.3E−1); desmosome (1.75, 3.1E−5, 1.7E−3); cell junction (1.75, 2.2E−4, 9.6E−3); intercellular junction (1.75, 1.6E−3, 4.7E−2); apicolateral plasma membrane (1.75, 1.9E−3, 5.2E−2); apical junction complex (1.75, 1.9E−3, 5.2E−2); sh3 domain (1.72, 2.1E−3, 5.0E−2); 710 signal (3.82, 2.4E−8, 9.0E−6); glycoprotein (3.82, 6.0E−7, c c c c g c g c c 1.7E−4); extracellular region (3.82, 1.8E−6, 1.1E−3); A % 6.901 8.309 4.084 25.91 3.521 7.183 36.61 3.802 0.845 extracellular space (3.82, 1.9E−6, 5.9E−4); protein T % 9.295 17.60 2.816 17.04 22.39 7.887 1.408 3.661 0.422 metabolism (3.82, 2.1E−4, 1.4E−1; serine/threonine-protein G % 41.69 30.56 40 15.91 45.77 15.91 58.87 8.450 1.549 kinase (2.4, 1.2E−5, 1.7E−3); transferase (2.4, 1.7E−5, 1.8E−3); C % 42.11 43.52 53.09 41.12 28.30 69.01 3.098 84.08 97.18 atp-binding (2.4, 7.7E−5, 6.7E−3); kinase (2.4, 2.6E−4, 1.7E−2); protein serine/threonine kinase activity (2.4, 6.6E−4, 2.7E−1); protein-tyrosine kinase activity (2.4, 1.0E−3, 3.4E−1); protein amino acid phosphorylation (2.4, 1.3E−3, 3.0E−1); S_TKc (2.4, 2.0E−3, 6.9E−1); protein kinase activity (2.4, 2.2E−3, 4.4E−1); phosphorylation (2.4, 3.7E−3, 5.1E−1); cAMP-dependent protein kinase activity (2.4, 4.2E−3, 5.4E−1); cyclic nucleotide-dependent protein kinase activity (2.4, 4.2E−3, 5.4E−1); protein kinase CK2 activity (2.4, 4.4E−3, 5.4E−1); phosphotransferase activity, alcohol group as acceptor (2.4, 5.6E−3, 6.0E−1); ATP (2.4, 1.3E−2, 2.8E−1); serine/threonine-specific protein kinase (2.4, 5.6E−2, 6.3E−1); phosphotransferase (2.4, 5.9E−2, 6.3E−1); negative regulation of biological process (2.38, 9.3E−4, 2.5E−1); negative regulation of cellular process (2.38, 1.6E−3, 3.3E−1); negative regulation of physiological process (2.38, 1.7E−3, 3.4E−1); negative regulation of cellular physiological process (2.38, 3.2E−3, 4.7E−1); extrinsic to plasma membrane (2.25, 1.2E−3, 1.7E−1); extrinsic to membrane (2.25, 1.7E−3, 1.9E−1); protein transport (2.22, 4.6E−5, 4.4E−3); cell organization and biogenesis (2.22, 9.0E−5, 1.0E−1); protein localization (2.22, 6.1E−3, 6.7E−1); metal-binding (2.11, 4.4E−6, 8.4E−4); cell cycle (1.85, 6.5E−3, 2.0E−1); anti-oncogene (1.85, 1.6E−2, 3.2E−1); electron transfer (1.77, 3.3E−4, 2.0E−2); chromoprotein (1.77, 5.8E−4, 3.1E−2); heme (1.77, 1.4E−3, 5.8E−2); heme binding (1.77, 2.6E−3, 4.3E−1); tetrapyrrole binding (1.77, 2.6E−3, 4.3E−1); metalloprotein (1.77, 3.2E−3, 1.2E−1); unspecific monooxygenase activity (1.77, 6.7E−3, 6.2E−1); iron (1.77, 7.4E−3, 2.1E−1); monooxygenase (1.77, 1.2E−2, 2.8E−1); oxidoreductase (1.77, 1.9E−2, 3.4E−1); microsome (1.77, 4.1E−2, 5.5E−1); golgi stack (1.69, 2.1E−3, 8.1E−2); transport (1.64, 7.3E−4, 3.7E−2); 397 metal-binding (3.5, 6.9E−6, 2.6E−3); zinc (3.5, 5.6E−5, 1.1E−2); c g g a g g a a g cation binding (3.5, 1.8E−4, 1.3E−1); ion binding (3.5, A % 21.41 18.63 5.541 44.33 25.94 8.816 96.22 89.42 5.541 3.2E−4, 1.8E−1); metal ion binding (3.5, 3.2E−4, 1.8E−1); T % 13.09 13.85 5.793 13.85 9.571 8.060 0 0.251 1.511 transition metal ion binding (3.5, 9.0E−4, 3.0E−1); zinc ion G % 29.97 48.61 61.96 37.27 59.94 64.23 1.763 8.060 89.16 binding (3.5, 3.2E−3, 6.3E−1); zinc-finger (3.5, 5.0E−3, C % 35.51 18.89 26.70 4.534 4.534 18.89 2.015 2.267 3.778 3.3E−1); nuclear protein (3.19, 1.5E−8, 1.7E−5); organelle (3.19, 2.8E−4, 1.6E−1); intracellular organelle (3.19, 6.5E−4, 1.8E−1); membrane-bound organelle (3.19, 1.1E−3, 2.1E−1); intracellular membrane-bound organelle (3.19, 2.4E−3, 3.1E−1); nucleus (3.19, 3.3E−3, 3.4E−1); intracellular (3.19, 5.4E−3, 4.3E−1); nuclear protein (2.99, 1.5E−8, 1.7E−5); regulation of metabolism (2.99, 9.4E−5, 8.2E−2); transcription (2.99, 1.7E−4, 2.4E−2); regulation of physiological process (2.99, 2.1E−4, 1.0E−1); regulation of biological process (2.99, 2.2E−4, 9.5E−2);); transcription, DNA-dependent (2.99, 1.0E−3, 2.1E−1);; metalloexopeptidase activity (1.22, 2.1E−3, 5.2E−1); glycosyltransferase (1.09, 2.1E−2, 6.9E−1); calcium (1.09, 1.4E−2, 5.6E−1); transport (0.96, 1.3E−2, 5.4E−1); nucleotide-binding (0.43, 2.2E−2, 6.9E−1); 357 ribosomal protein (4.97, 2.6E−8, 3.0E−5); ribonucleoprotein g c g g c c a g g (4.97, 3.8E−8, 2.2E−5); macromolecule biosynthesis (4.97, A % 5.602 18.76 23.80 5.322 3.081 1.960 88.51 40.61 3.361 4.6E−7, 8.3E−4); cellular biosynthesis (4.97, 5.9E−7, 7.2E−4); T % 4.761 5.602 6.162 5.882 18.76 1.120 1.400 3.081 2.801 biosynthesis (4.97, 7.1E−7, 6.4E−4); structural G % 71.70 29.69 35.57 63.30 3.361 3.641 5.882 52.10 70.86 constituent of ribosome (4.97, 2.3E−6, 5.5E−3); ribosome C % 17.92 45.93 34.45 25.49 74.78 93.27 4.201 4.201 22.96 (4.97, 4.5E−6, 9.2E−4); RIBOSOME (4.97, 5.8E−6, 1.1E−3); protein biosynthesis (4.97, 1.6E−5, 1.1E−2); ribonucleoprotein complex (4.97, 6.6E−5, 1.0E−2); structural molecule activity (4.97, 9.4E−4, 4.3E−1); transport (3.53, 1.1E−4, 2.5E−2); transporter activity (3.53, 1.2E−4, 1.3E−1); establishment of localization (3.53, 5.0E−4, 2.3E−1); transport (3.53, 5.7E−4, 2.0E−1); localization (3.53, 6.4E−4, 2.1E−1); 373 cytoplasm (2.19, 6.6E−5, 4.0E−2); intracellular (2.19, 1.2E−3, g g c c c a g c c 3.1E−1); intracellular organelle (2.19, 4.3E−3, 4.8E−1); A % 13.13 7.774 2.680 22.25 6.702 52.54 1.340 6.970 0.536 organelle (2.19, 4.6E−3, 4.3E−1); Defensin propeptide (2.16, T % 6.702 21.17 2.949 22.52 2.412 10.72 0.536 9.115 3.485 1.1E−4, 4.3E−1); membrane (2.04, 5.8E−9, 6.6E−6); G % 49.06 58.98 9.383 20.64 36.19 24.39 96.51 3.217 2.680 glycoprotein (2.04, 2.0E−5, 1.1E−2); transmembrane (2.04, C % 31.09 12.06 84.98 34.58 54.69 12.33 1.608 80.69 93.29 5.2E−5, 2.0E−2); signal (2.04, 1.8E−3, 2.0E−1); extracellular region (2.04, 6.0E−3, 4.1E−1); extracellular space (2.04, 8.8E−3, 4.9E−1); ribosomal protein (1.99, 6.8E−5, 1.9E−2); structural constituent of ribosome (1.99, 7.5E−4, 6.0E−1); ribonucleoprotein (1.99, 1.3E−3, 1.7E−1); ribosome (1.99, 5.0E−3, 4.0E−1); heparin binding (1.94, 1.8E−3, 6.6E−1); heparin-binding (1.94, 1.2E−2, 5.0E−1); sh3 domain (1.93, 1.8E−3, 1.9E−1); transit peptide (1.69, 2.2E−3, 1.9E−1); mitochondrion (1.69, 3.6E−3, 5.2E−1); mitochondrion (1.69, 4.8E−3, 3.2E−1); organelle inner membrane (1.69, 1.4E−2, 6.3E−1); gtp-binding (1.64, 6.1E−4, 9.5E−2); nucleotide- binding (1.35, 4.4E−3, 3.2E−1); transport (1.32, 2.2E−3, 2.0E−1); heat shock (1.03, 1.9E−2, 6.4E−1); zinc (1.01, 8.8E−3, 4.5E−1); metal-binding (1.01, 1.1E−2, 5.0E−1); ubl conjugation pathway (0.64, 1.1E−2, 4.8E−1); nuclear protein (0.29, 2.2E−2, 6.9E−1); 290 cytolysis (3.78, 1.5E−7, 1.7E−4); SF001135: trypsin (3.78, g c t g g g a a g 4.3E−7, 9.3E−4); serine proteinase (3.78, 1.0E−6, 5.9E−4); A % 8.965 2.068 25.17 3.448 26.55 7.241 75.51 82.75 11.72 serine protease (3.78, 1.8E−6, 6.7E−4); cytolysis (3.78, T % 6.551 2.068 38.27 0.689 12.06 2.068 1.034 3.103 1.034 4.5E−6, 1.6E−2); protease (3.78, 1.3E−5, 3.6E−3); zymogen G % 61.72 32.41 11.37 93.44 47.58 80.68 22.41 11.03 83.79 (3.78, 3.4E−5, 7.6E−3); Peptidase S1A, chymotrypsin (3.78, C % 22.75 63.44 25.17 2.413 13.79 10 1.034 3.103 3.448 7.1E−5, 8.7E−2); Tryp_SPc (3.78, 9.7E−5, 2.8E−2); domain: Peptidase S1 (3.78, 1.1E−4, 2.9E−1); serine-type endopeptidase activity (3.78, 1.5E−4, 3.0E−1); Peptidase S1 and S6, chymotrypsin/Hap (3.78, 1.8E−4, 1.4E−1); serine- type peptidase activity (3.78, 2.3E−4, 2.5E−1); proteolysis (3.78, 1.7E−3, 4.9E−1); t-cell (3.78, 2.1E−3, 1.7E−1); peptidase activity (3.78, 2.4E−3, 6.9E−1); hydrolase (3.78, 2.5E−3, 1.7E−1); direct protein sequencing (3.78, 4.6E−3, 2.5E−1); SF001714: Bcl2 related apoptosis regulator (3.2, 3.1E−6, 3.4E−3); Bcl2 related apoptosis regulator (3.2, 4.4E−6, 2.3E−2); Apoptosis regulator, Bcl-2 related (3.2, 7.3E−6, 1.9E−2); BCL (3.2, 1.9E−5, 1.1E−2); BCL2-like apoptosis inhibitor (3.2, 2.7E−5, 4.6E−2); Apoptosis regulator Bcl-2, BH (3.2, 1.1E−4, 1.1E−1); cell death (3.2, 3.2E−4, 1.5E−1); death (3.2, 3.8E−4, 1.6E−1); cellular physiological process (2.27, 1.6E−5, 2.9E−2); protein metabolism (2.27, 7.7E−5, 8.9E−2); cellular macromolecule metabolism (2.27, 7.9E−5, 6.9E−2); cellular protein metabolism (2.27, 1.1E−4, 7.4E−2); macromolecule metabolism (2.27, 2.4E−4, 1.4E−1); nuclear protein (1.62, 4.1E−3, 2.4E−1); signal (1.61, 7.7E−5, 1.2E−2); glycoprotein (1.61, 3.7E−4, 4.6E−2); metal-binding (1.41, 1.4E−3, 1.2E−1); zinc (1.41, 2.2E−3, 1.6E−1); zinc- finger (1.41, 1.2E−2, 4.0E−1); nuclear pore (1.38, 9.6E−3, 6.9E−1); pore complex (1.38, 9.6E−3, 6.9E−1); chaperone (1.17, 2.3E−2, 6.0E−1); cell cycle (1.06, 1.2E−2, 4.0E−1); nucleotide-binding (1.02, 6.5E−3, 3.1E−1); transferase (1.02, 7.7E−3, 3.4E−1); atp-binding (1.02, 2.0E−2, 5.7E−1); membrane (0.95, 7.8E−4, 7.8E−2); transmembrane (0.95, 1.0E−2, 3.8E−1); glycosyltransferase (0.92, 5.7E−3, 2.9E−1); rna-binding (0.92, 7.9E−3, 3.4E−1); glycosyltransferase (0.91, 5.7E−3, 2.9E−1); ligase (0.84, 3.0E−3, 2.0E−1); 283 Serpin B9 and maspin (3.42, 3.1E−8, 1.6E−4); protease t c c t c c a c c inhibitor activity (3.42, 3.0E−4, 5.1E−1); Proteinase A % 20.84 26.50 6.713 18.37 7.067 5.653 96.11 4.240 1.413 inhibitor I4, serpin (3.42, 3.2E−4, 5.7E−1); enzyme inhibitor T % 37.80 30.03 17.66 49.82 4.240 2.120 0.353 35.68 1.766 activity (3.42, 6.4E−4, 4.0E−1); SERPIN (3.42, 7.0E−4, G % 20.49 12.72 30.38 20.14 10.60 3.886 2.473 13.78 1.766 1.8E−1); endopeptidase inhibitor activity (3.42, 1.4E−3, C % 20.84 30.74 45.22 11.66 78.09 88.33 1.060 46.28 95.05 5.8E−1); transport (2.84, 7.3E−5, 2.1E−2); transporter activity (2.84, 4.7E−4, 4.3E−1); membrane (2.77, 1.2E−9, 1.4E−6); transmembrane (2.77, 4.6E−6, 2.6E−3); oxygen carrier (1.69, 1.3E−3, 2.2E−1); oxygen transport (1.69, 1.6E−3, 2.3E−1); oxygen transporter activity (1.69, 3.2E−3, 6.7E−1); ribosomal protein (1.67, 8.7E−4, 1.8E−1); structural constituent of ribosome (1.67, 4.8E−3, 6.9E−1); ribosome (1.67, 5.3E−3, 6.6E−1); ribonucleoprotein (1.67, 1.0E−2, 6.0E−1); cytokine (1.07, 2.4E−3, 2.9E−1); glycoprotein (0.92, 2.9E−3, 3.1E−1); signal (0.92, 3.8E−3, 3.5E−1); cytokine (0.74, 2.4E−3, 2.9E−1); protease (0.66, 8.0E−3, 5.3E−1); 220 protein transport (2.99, 3.7E−5, 1.4E−2); golgi stack (2.99, g g c g g c g g g 3.0E−4, 6.7E−2); Golgi stack (2.99, 2.9E−3, 2.2E−1); A % 6.363 3.181 5.909 7.727 4.090 0.454 21.81 15.45 3.181 transport (2.99, 3.8E−3, 3.2E−1); Golgi apparatus (2.99, T % 2.727 0.454 3.636 7.272 4.545 0.909 0.909 4.090 1.818 9.0E−3, 5.0E−1); cellular physiological process (2.46, 4.8E−6, G % 81.36 67.72 8.636 77.27 90.45 3.181 75.90 49.54 50.90 1.7E−2); nuclear protein (2.46, 2.4E−5, 2.7E−2); C % 9.545 28.63 81.81 7.727 0.909 95.45 1.363 30.90 44.09 intracellular (2.46, 5.7E−5, 3.5E−2); intracellular organelle (2.46, 3.7E−4, 1.1E−1); organelle (2.46, 3.9E−4, 7.7E−2); intracellular membrane-bound organelle (2.46, 1.6E−3, 2.2E−1); membrane-bound organelle (2.46, 1.7E−3, 1.6E−1); mrna processing (2.07, 3.3E−4, 6.1E−2); mrna splicing (2.07, 5.4E−3, 3.8E−1); nuclear protein (1.45, 2.4E−5, 2.7E−2); dna-binding (1.45, 5.4E−4, 7.4E−2); transcription (1.45, 2.0E−2, 6.7E−1); protein transport (1.44, 3.7E−5, 1.4E−2); transport (1.44, 3.8E−3, 3.2E−1); lipoprotein (1.42, 4.6E−4, 7.2E−2); gtp-binding (1.42, 5.5E−3, 3.6E−1); transit peptide (1.34, 3.1E−3, 3.2E−1); cell cycle (1.25, 9.0E−3, 4.8E−1); transcription (1.13, 2.0E−2, 6.7E−1); endoplasmic reticulum (0.97, 1.9E−2, 6.7E−1); gtp-binding (0.91, 5.5E−3, 3.6E−1); nucleotide-binding (0.91, 2.2E−2, 6.8E−1); ubl conjugation pathway (0.76, 7.1E−3, 4.2E−1); membrane (0.32, 3.5E−3, 3.3E−1); 127 membrane (1.49, 4.0E−4, 2.0E−1); transmembrane (1.49, g c c g g g g c c 5.1E−4, 1.8E−1); nuclear protein (0.83, 8.3E−5, 9.0E−2); A % 7.086 3.937 1.574 2.362 18.11 0.787 2.362 2.362 0.787 T % 18.11 3.937 26.77 18.11 8.661 6.299 0.787 0 3.149 G % 49.60 18.89 32.28 77.16 65.35 90.55 92.91 0.787 1.574 C % 25.19 73.22 39.37 2.362 7.874 2.362 3.937 96.85 94.48 120 monooxygenase activity (1.71, 2.4E−4, 2.6E−1); a g c c t c a c c monooxygenase (1.71, 8.4E−4, 6.2E−1); A % 91.66 0 13.33 6.666 15 9.166 45.83 2.5 6.666 T % 3.333 2.5 21.66 25 75 20 3.333 4.166 3.333 G % 0.833 97.5 31.66 22.5 2.5 20 43.33 8.333 4.166 C % 4.166 0 33.33 45.83 7.5 50.83 7.5 85 85.83 119 embryonic morphogenesis (1.41, 5.3E−5, 1.7E−1); g c g g c g g c c A % 0.840 1.680 21.84 0.840 27.73 2.521 3.361 15.12 5.882 T % 5.042 0.840 1.680 0.840 1.680 4.201 0 4.201 5.042 G % 79.83 1.680 75.63 92.43 5.042 56.30 88.23 11.76 35.29 C % 14.28 95.79 0.840 5.882 65.54 36.97 8.403 68.90 53.78 118 glycoprotein (1.29, 6.3E−4, 5.2E−1); membrane(1.29, 7.1E−4, g g c a g c a g g 3.3E−1); A % 23.72 31.35 6.779 44.91 11.86 4.237 90.67 0.847 11.86 T % 26.27 20.33 16.10 33.89 22.03 0 3.389 0 7.627 G % 44.91 47.45 24.57 0.847 55.93 0.847 0.847 95.76 78.81 C % 5.084 0.847 52.54 20.33 10.16 94.91 5.084 3.389 1.694 118 Vomeronasal receptor, type 2 (2.64, 1.7E−7, 8.9E−4); g c t g c a g a c metabotropic glutamate receptor signaling pathway (2.64, A % 5.932 4.237 15.25 0.847 8.474 85.59 1.694 83.89 22.88 3.3E−7, 1.2E−3); glutamate signaling pathway (2.64, 6.3E−7, T % 28.81 20.33 63.55 0.847 5.932 11.01 0 5.084 0.847 1.1E−3); metabotropic glutamate, GABA-B-like receptor G % 33.89 25.42 7.627 90.67 0 0.847 96.61 4.237 10.16 activity (2.64, 2.9E−6, 6.9E−3); Extracellular ligand-binding C % 31.35 50 13.55 7.627 85.59 2.542 1.694 6.779 66.10 receptor (2.64, 4.6E−6, 1.2E−2); glutamate receptor activity (2.64, 1.4E−5, 1.6E−2); GPCR, family 3, metabotropic glutamate receptor-like (2.64, 5.6E−5, 9.1E−2); cytoskeletal protein binding (1.66, 7.8E−4, 4.7E−1); 113 glycosidase (2.36, 1.0E−3, 4.5E−1); developmental protein g c c g g c a g c (0.91, 2.1E−3, 3.9E−1); membrane (0.89, 1.5E−3, 3.5E−1); A % 3.539 10.61 4.424 25.66 7.079 0 84.95 0 2.654 T % 0.884 0.884 16.81 9.734 7.079 1.769 5.309 29.20 8.849 G % 95.57 7.964 17.69 45.13 58.40 1.769 0.884 70.79 1.769 C % 0 80.53 61.06 19.46 27.43 96.46 8.849 0 86.72 111 natural killer cell lectin-like receptor binding (2.6, 1.6E−6, g a a g a c a g c 3.8E−3); anchored to plasma membrane (2.6, 1.0E−5, 6.4E−3); A % 1.801 93.69 100 11.71 36.03 7.207 99.09 23.42 19.81 anchored to membrane (2.6, 1.0E−5, 6.4E−3); lipoprotein T % 7.207 0 0 3.603 6.306 2.702 0 9.009 3.603 (2.6, 2.5E−4, 2.5E−1); NATURAL KILLER CELL G % 71.17 0 0 79.27 29.72 6.306 0.900 38.73 34.23 MEDIATED CYTOTOXICITY (2.6, 1.0E−3, 1.8E−1); C % 19.81 6.306 0 5.405 27.92 83.78 0 28.82 42.34 membrane (0.65, 1.5E−3, 5.8E−1); 108 signal (0.95, 3.5E−3, 6.4E−1); dna-binding (0.68, 9.7E−4, c c t g g c a g c 6.7E−1); nuclear protein (0.68, 1.7E−3, 6.3E−1); A % 6.481 11.11 1.851 1.851 19.44 37.03 95.37 20.37 4.629 T % 12.96 5.555 94.44 1.851 14.81 4.629 2.777 29.62 3.703 G % 29.62 2.777 0.925 96.29 62.03 6.481 0.925 42.59 25 C % 50.92 80.55 2.777 0 3.703 51.85 0.925 7.407 66.66 95 transmembrane (1.46, 7.0E−5, 7.7E−2); membrane (1.46, g c g c t g g c c 1.9E−4, 1.0E−1); glycoprotein (1.46, 2.9E−3, 5.6E−1); A % 5.263 24.21 8.421 1.052 1.052 9.473 0 4.210 2.105 developmental protein (1.2, 1.5E−3, 4.3E−1); developmental T % 5.263 5.263 22.10 3.157 48.42 3.157 0 4.210 4.210 protein (0.99, 1.5E−3, 4.3E−1); glycoprotein (0.62, 2.9E−3, G % 87.36 4.210 36.84 2.105 24.21 87.36 100 8.421 23.15 5.6E−1); C % 2.105 66.31 32.63 93.68 26.31 0 0 83.15 70.52 94 cellular physiological process (2.39, 2.6E−6, 9.3E−3); c t g c a g g c c A % 3.191 3.191 1.063 4.255 39.36 7.446 43.61 6.382 0 T % 3.191 87.23 6.382 15.95 38.29 7.446 2.127 4.255 1.063 G % 3.191 2.127 89.36 37.23 19.14 85.10 50 13.82 0 C % 90.42 7.446 3.191 42.55 3.191 0 4.255 75.53 98.93 86 Carboxylesterase, type B (1.63, 7.1E−5, 3.1E−1); serine c c t c c c a c c esterase (1.63, 1.0E−4, 1.1E−1); A % 1.162 23.25 4.651 5.813 10.46 24.41 91.86 27.90 2.325 T % 3.488 5.813 58.13 30.23 33.72 4.651 2.325 1.162 1.162 G % 0 0 0 1.162 2.325 1.162 5.813 10.46 4.651 C % 95.34 70.93 37.20 62.79 53.48 69.76 0 60.46 91.86 83 Keratin, high sulfur B2 protein (2.49, 1.6E−5, 7.9E−2); t c t g gc c g c c keratin filament (2.49, 3.4E−5, 2.1E−2); A % 9.638 1.204 7.228 1.204 21.68 18.07 24.09 1.204 0 T % 87.95 4.819 86.74 1.204 18.07 16.86 1.204 4.819 3.614 G % 0 0 4.819 92.77 30.12 10.84 67.46 3.614 8.433 C % 2.409 93.97 1.204 4.819 30.12 54.21 7.228 90.36 87.95 80 glycoprotein (1.16, 1.1E−4, 1.2E−1); signal (1.16, 8.4E−4, c g c c c c g c g 3.8E−1); A % 1.25 3.75 10 1.25 3.75 6.25 2.5 8.75 0 T % 1.25 6.25 0 1.25 2.5 17.5 2.5 0 7.5 G % 12.5 86.25 12.5 36.25 8.75 15 92.5 7.5 92.5 C % 85 3.75 77.5 61.25 85 61.25 2.5 83.75 0 79 zymogen (1.33, 4.6E−4, 4.1E−1); c a g g t c a c c A % 36.70 96.20 21.51 2.531 7.594 11.39 81.01 1.265 1.265 T % 16.45 1.265 10.12 10.12 82.27 1.265 1.265 2.531 1.265 G % 5.063 2.531 51.89 67.08 6.329 36.70 5.063 0 0 C % 41.77 0 16.45 20.25 3.797 50.63 12.65 96.20 97.46 79 extracellular region (1.18, 6.1E−4, 3.1E−1); extracellular g g a g t c a c c space (1.18, 1.3E−3, 3.2E−1); A % 1.265 5.063 94.93 13.92 3.797 1.265 73.41 5.063 2.531 T % 26.58 1.265 3.797 3.797 58.22 3.797 1.265 3.797 1.265 G % 69.62 93.67 1.265 70.88 37.97 22.78 18.98 12.65 0 C % 2.531 0 0 11.39 0 72.15 6.329 78.48 96.20 70 transmembrane (0.82, 1.6E−3, 6.0E−1); g c c a g a ag ag c A % 10 10 5.714 85.71 4.285 72.85 45.71 31.42 7.142 T % 18.57 0 15.71 4.285 0 2.857 0 21.42 1.428 G % 42.85 2.857 2.857 2.857 78.57 15.71 45.71 31.42 8.571 C % 28.57 87.14 75.71 7.142 17.14 8.571 8.571 15.71 82.85 68 membrane (1.58, 1.1E−4, 1.1E−1); glycoprotein (1.5, 1.1E−3, a a a g g g a g Vg 4.8E−1); signal (1.5, 1.2E−3, 3.6E−1); A % 85.29 54.41 91.17 48.52 1.470 44.11 97.05 32.35 26.47 T % 7.352 0 2.941 1.470 0 0 1.470 1.470 4.411 G % 1.470 29.41 4.411 50 94.11 51.47 1.470 66.17 54.41 C % 5.882 16.17 1.470 0 4.411 4.411 0 0 14.70 67 FBOX (1.74, 2.8E−9, 1.6E−6); Cyclin-like F-box (1.74, c c a c t c a a g 1.5E−7, 7.7E−4); ubl conjugation pathway (1.74, 2.2E−3, A % 25.37 2.985 94.02 2.985 16.41 4.477 98.50 62.68 5.970 5.7E−1); glycoprotein (1.52, 6.6E−4, 5.3E−1); signal (1.52, T % 13.43 7.462 2.985 7.462 53.73 1.492 0 2.985 1.492 1.9E−3, 6.6E−1); glycoprotein (0.95, 6.6E−4, 5.3E−1); G % 26.86 0 2.985 8.955 1.492 37.31 0 31.34 92.53 C % 34.32 89.55 0 80.59 28.35 56.71 1.492 2.985 0 64 hydrolase (1.95, 1.6E−4, 1.6E−1); c a g c t c a g c A % 20.31 73.43 6.25 7.812 0 1.562 95.31 7.812 4.687 T % 1.562 14.06 3.125 28.12 71.87 0 0 7.812 1.562 G % 9.375 9.375 48.43 7.812 20.31 0 0 81.25 1.562 C % 68.75 3.125 42.18 56.25 7.812 98.43 4.687 3.125 92.18 62 SCY (1.28, 1.9E−3, 6.8E−1); transferase (0.95, 1.7E−4, 1.7E−1); a c c a g c a t c A % 69.35 25.80 4.838 95.16 12.90 9.677 74.19 9.677 0 T % 3.225 20.96 3.225 1.612 27.41 11.29 1.612 87.09 0 G % 11.29 1.612 24.19 3.225 35.48 12.90 22.58 1.612 0 C % 16.12 51.61 67.74 0 24.19 66.12 1.612 1.612 100 61 transport (1.77, 4.7E−4, 2.4E−1); membrane (0.98, 1.8E−4, c g g g c g g c c 1.9E−1); A % 18.03 31.14 11.47 8.196 1.639 3.278 1.639 1.639 0 T % 8.196 0 16.39 4.918 3.278 27.86 1.639 1.639 0 G % 6.557 49.18 70.49 78.68 0 54.09 68.85 0 11.47 C % 67.21 19.67 1.639 8.196 95.08 14.75 27.86 96.72 88.52 61 Hormone (4.77, 4.2E−13, 4.8E−10); Somatotropin hormone ag c c g c a g a g (4.77, 7.6E−12, 3.9E−8); Cytokine, four-helical bundle A % 34.42 3.278 0 21.31 3.278 91.80 11.47 63.93 6.557 (4.77, 1.7E−10, 4.3E−7); hormone activity (4.77, 2.4E−8, T % 22.95 1.639 6.557 27.86 0 0 1.639 3.278 11.47 5.8E−5); receptor binding (4.77, 1.2E−5, 9.5E−3); G % 34.42 4.918 0 34.42 4.918 3.278 85.24 6.557 81.96 Glycoprotein (4.77, 1.2E−5, 7.0E−3); Signal (4.77, 3.9E−5, C % 8.196 90.16 93.44 16.39 91.80 4.918 1.639 26.22 0 1.5E−2); extracellular space (4.77, 5.2E−4, 2.7E−1); extracellular region (4.77, 1.8E−3, 4.2E−1); 55 nucleotide-binding (1.94, 1.2E−4, 1.3E−1); adenyl c t c g g g a c c nucleotide binding (1.94, 1.6E−4, 3.1E−1); protein amino A % 3.636 1.818 9.090 0 1.818 12.72 94.54 21.81 3.636 acid phosphorylation (1.94, 1.6E−4, 4.4E−1); purine T % 25.45 87.27 16.36 3.636 1.818 5.454 0 3.636 0 nucleotide binding (1.94, 2.3E−4, 2.5E−1); phosphorylation G % 16.36 10.90 0 94.54 94.54 41.81 5.454 1.818 5.454 (1.94, 4.0E−4, 5.2E−1); atp-binding (1.94, 5.4E−4, 2.6E−1); C % 54.54 0 74.54 1.818 1.818 40 0 72.72 90.90 ATP binding (1.94, 5.5E−4, 2.8E−1); nucleotide binding (1.94, 7.6E−4, 3.1E−1); phosphorus metabolism (1.94, 1.2E−3, 6.5E−1); phosphate metabolism (1.94, 1.2E−3, 6.5E−1); kinase activity (1.94, 1.3E−3, 4.2E−1); transferase activity, transferring phosphorus-containing groups (1.94, 3.1E−3, 6.1E−1); kinase (1.94, 3.6E−3, 6.5E−1); receptor binding (1.84, 4.3E−4, 2.9E−1); transmembrane protein (0.53, 2.0E−3, 5.2E−1); 52 E-class P450, CYP3A (2.21, 4.0E−7, 2.1E−3); g a a g c a g a g METABOLISM OF XENOBIOTICS BY CYTOCHROME A % 11.53 78.84 59.61 1.923 1.923 84.61 0 76.92 5.769 P450 (2.21, 2.1E−5, 4.1E−3); GAMMA- T % 1.923 3.846 5.769 1.923 3.846 1.923 0 9.615 3.846 HEXACHLOROCYCLOHEXANE DEGRADATION G % 51.92 5.769 30.76 57.69 3.846 3.846 98.07 11.53 86.53 (2.21, 2.2E−5, 2.2E−3); LINOLEIC ACID METABOLISM C % 34.61 11.53 3.846 38.46 90.38 9.615 1.923 1.923 3.846 (2.21, 1.9E−4, 1.2E−2); 50 KRAB box (1.94, 1.3E−6, 6.6E−3); nuclear protein (1.94, t t c t c t g ag c 3.6E−5, 4.0E−2); ZnF_C2H2 (1.94, 4.4E−5, 2.5E−2); Zinc A % 30 0 4 6 4 0 16 36 2 finger, C2H2-type (1.94, 5.3E−5, 1.3E−1); zinc-finger (1.94, T % 44 94 4 68 2 98 0 12 6 6.3E−5, 3.5E−2); KRAB (1.94, 8.0E−5, 2.3E−2); zinc (1.94, G % 8 6 2 6 8 0 84 36 6 3.1E−4, 1.1E−1); metal-binding (1.94, 3.4E−4, 9.4E−2); C % 18 0 90 20 86 2 0 16 86 General function prediction only (1.94, 1.7E−2, 6.9E−1); hyalurononglucosaminidase activity (1.8, 7.9E−5, 1.7E−1); Glycoside hydrolase, family 56 (1.8, 8.0E−5, 1.3E−1); hexosaminidase activity (1.8, 3.9E−4, 3.8E−1); 46 intracellular membrane-bound organelle (2.38, 4.2E−4, c c t g c a g c t 2.3E−1); membrane-bound organelle (2.38, 4.3E−4, 1.2E−1); A % 10.86 4.347 6.521 0 10.86 97.82 0 6.521 10.86 intracellular organelle (2.38, 2.3E−3, 3.8E−1); organelle T % 0 4.347 84.78 2.173 21.73 0 0 10.86 54.34 (2.38, 2.4E−3, 3.1E−1); intracellular (2.38, 4.7E−3, 4.4E−1); G % 0 8.695 4.347 97.82 17.39 2.173 89.13 8.695 30.43 nucleus (2.38, 6.1E−3, 4.6E−1); dna-binding (0.93, 9.5E−4, C % 89.13 82.60 4.347 0 50 0 10.86 73.91 4.347 6.6E−1); nucleus (0.93, 6.1E−3, 4.6E−1); 42 integrin complex (1.32, 1.6E−3, 6.3E−1); a c t g c c a c c A % 100 7.142 4.761 19.04 38.09 19.04 95.23 35.71 2.380 T % 0 16.66 95.23 0 2.380 26.19 2.380 4.761 2.380 G % 0 11.90 0 78.57 14.28 9.523 2.380 11.90 0 C % 0 64.28 0 2.380 45.23 45.23 0 47.61 95.23 42 Tryp_SPc (2.24, 3.0E−4, 1.6E−1); serine protease (2.24, g c t g c c a c a 1.0E−3, 6.9E−1); A % 9.523 2.380 26.19 2.380 4.761 2.380 100 38.09 88.09 T % 23.80 9.523 73.80 0 21.42 2.380 0 2.380 7.142 G % 38.09 0 0 97.61 2.380 2.380 0 0 4.761 C % 28.57 88.09 0 0 71.42 92.85 0 59.52 0 41 defense response to bacteria (3.53, 5.5E−8, 2.0E−4); Beta g c t t c a g t c defensin (3.53, 7.7E−8, 4.0E−4); response to bacteria (3.53, A % 2.439 0 4.878 0 0 46.34 17.07 14.63 0 1.4E−7, 2.4E−4); defensin (3.53, 3.4E−7, 3.9E−4); antibiotic T % 31.70 4.878 92.68 90.24 0 41.46 0 58.53 0 (3.53, 4.7E−7, 2.7E−4); antimicrobial (3.53, 7.3E−7, 2.8E−4); G % 60.97 14.63 2.439 2.439 2.439 4.878 80.48 0 0 response to stress (3.53, 5.0E−4, 4.6E−1); C % 4.878 80.48 0 7.317 97.56 7.317 2.439 26.82 100 41 transmembrane (1.47, 5.4E−4, 4.6E−1); membrane (1.47, g a c c c c a c c 2.2E−3, 5.7E−1); glycoprotein (1.19, 6.8E−4, 3.2E−1); A % 0 60.97 2.439 0 12.19 2.439 78.04 4.878 2.439 plasma (1.19, 4.0E−3, 6.8E−1); T % 12.19 4.878 2.439 0 19.51 2.439 2.439 0 114.63 G % 82.92 34.14 2.439 2.439 24.39 0 19.51 0 7.317 C % 4.878 0 92.68 97.56 43.90 95.12 0 95.12 75.60 40 UDP-glucuronosyl/UDP-glucosyltransferase (2.59, 3.5E−6, a t t t t c a a g 1.8E−2); SF005678: glucuronosyltransferase (2.59, 3.7E−6, A % 57.5 0 17.5 5 0 5 100 50 0 8.0E−3); T % 12.5 67.5 82.5 65 95 0 0 2.5 5 G % 7.5 2.5 0 0 2.5 10 0 47.5 87.5 C % 22.5 30 0 30 2.5 85 0 0 7.5 38 SF005558: natural killer cell receptor P1 (4.28, 2.1E−12, a c t c c c a a a 4.4E−9); lectin (4.28, 1.0E−11, 1.2E−8); C-type lectin (4.28, A % 86.84 2.631 0 0 0 2.631 94.73 57.89 55.26 1.2E−10, 6.1E−7); CLECT (4.28, 1.3E−10, 7.4E−8); sugar T % 2.631 0 97.36 2.631 23.68 0 0 10.52 0 binding (4.28, 2.2E−9, 5.4E−6); t-cell (4.28, 8.0E−9, 4.6E−6); G % 10.52 28.94 0 5.263 0 10.52 5.263 10.52 39.47 carbohydrate binding (4.28, 3.0E−8, 3.6E−5); domain: C-type C % 0 68.42 2.631 92.10 76.31 86.84 0 21.05 5.263 lectin (4.28, 3.5E−8, 1.1E−4); antigen (4.28, 8.9E−8, 3.4E−5); signal-anchor (4.28, 5.5E−7, 1.6E−4); cell adhesion (4.28, 1.6E−5, 3.7E−3); receptor (4.28, 2.9E−5, 5.5E−3); glycoprotein (4.28, 4.1E−4, 6.4E−2); multigene family (4.28, 7.1E−4, 9.6E−2); membrane (4.28, 1.5E−3, 1.7E−1); transmembrane (4.28, 6.5E−3, 5.2E−1);

TABLE 4 Emerging gene clusters which were identified by the clustering method pertaining Bos Tauros. The below clusters are arranged according to declining size. For each cluster, the table depicts the distribution of nucleotides for each position along the context sequence Distribution of nucleotides per position along the context sequence (%) Size of Cluster (number of genes) Function attributes set (Enrichment score/P_value/Benjamini) Pos: -9 -8 -7 -6 -5 -4 -3 -2 -1 815 structural molecule activity (2.4, 6.1E−5, 7.0E−2); g c c g c c a c c structural constituent of ribosome (2.4, 8.6E−5, 5.0E−2); A % 11.77 7.975 10.30 7.361 9.202 15.70 66.01 24.66 3.190 ribosome (2.4, 3.4E−4, 8.5E−2); ribosomal protein (2.4, T % 9.079 8.343 14.72 15.21 14.72 2.453 1.349 7.361 2.576 4.9E−4, 2.0E−1); ribonucleoprotein complex (2.4, 1.1E−3, 1.4E−1); G % 63.55 22.45 20.73 51.77 32.51 21.10 30.06 32.63 46.38 membrane (1.3, 4.8E−3, 3.9E−1); regulation of signal transduction C % 15.58 61.22 54.23 25.64 43.55 60.73 2.576 35.33 47.85 (1.03, 4.0E−3, 6.9E−1); regulation of signal transduction (0.87, 4.0E−3, 6.9E−1); intracellular non-membrane-bound organelle (0.79, 2.9E−3, 2.3E−1); non-membrane-bound organelle (0.79, 2.9E−3, 2.3E−1); lipoprotein (0.4, 6.9E−3, 4.4E−1); 583 intracellular non-membrane-bound organelle (1.78, 5.5E−3, g c c g c c g c c 5.2E−1); non-membrane-bound organelle (1.78, 5.5E−3, A % 10.29 7.375 8.404 9.777 4.459 27.78 26.07 3.945 1.715 5.2E−1); ribosome (1.78, 8.9E−3, 5.5E−1); pyridoxal T % 10.46 18.86 15.95 13.37 10.46 3.602 0.686 6.861 3.602 phosphate (1.23, 6.3E−3, 4.8E−1); homodimer (1.11, G % 49.91 20.75 27.95 65.00 21.09 10.97 70.32 1.886 8.747 1.6E−3, 2.5E−1); membrane (1.07, 8.7E−4, 2.3E−1); C % 29.33 53.00 47.68 11.83 63.97 57.63 2.915 87.30 85.93 glycoprotein (1.07, 1.3E−2, 6.2E−1); eye lens protein (1.03, 6.1E−3, 5.1E−1); kinase (0.69, 1.1E−2, 6.0E−1); cytoplasm (0.65, 1.5E−2, 6.4E−1); transit peptide (0.4, 7.5E−3, 5.0E−1); 474 Cathelicidin (2.77, 9.5E−6, 2.7E−2); antibiotic (2.77, c c c gc g c a c c 2.3E−4, 1.0E−1); antimicrobial (2.77, 5.7E−4, 1.0E−1); A % 24.26 3.797 13.08 23.62 10.54 8.016 55.27 3.797 3.375 pyrrolidone carboxylic acid (2.77, 2.0E−3, 2.7E−1); T % 15.18 18.35 13.50 11.81 15.82 8.438 3.797 2.531 4.008 fungicide (2.77, 8.2E−3, 6.6E−1); nucleotide-binding G % 18.14 36.28 21.51 32.27 40.08 18.35 36.70 6.962 4.008 (1.68, 1.5E−4, 1.3E−1); signal (0.96, 4.2E−4, 1.2E−1); C % 42.40 41.56 51.89 32.27 33.54 65.18 4.219 86.70 88.60 463 cellular macromolecule metabolism (2.17, 6.4E−4, 6.1E−1); g c c g c c g c c cellular physiological process (2.17, 7.0E−4, 4.0E−1); A % 8.639 3.239 9.503 7.343 2.807 39.95 20.51 4.967 0.431 cellular protein metabolism (2.17, 1.1E−3, 4.0E−1); T % 4.319 3.455 10.79 3.239 3.671 1.943 0.863 5.615 1.295 protein metabolism (2.17, 1.6E−3, 4.5E−1); structural G % 68.25 20.73 19.43 76.88 36.28 5.399 77.53 6.479 3.455 molecule activity (1.74, 1.7E−4, 1.9E−1); intracellular C % 18.79 72.57 60.25 12.52 57.23 52.69 1.079 82.93 94.81 non-membrane-bound organelle (1.74, 1.9E−3, 4.0E−1); non-membrane-bound organelle (1.74, 1.9E−3, 4.0E−1); cellular physiological process (1.47, 7.0E−4, 4.0E−1); structural molecule activity (1.32, 1.7E−4, 1.9E−1); intracellular non-membrane-bound organelle (1.32, 1.9E−3, 4.0E−1); non-membrane-bound organelle (1.32, 1.9E−3, 4.0E−1); homodimer (1.14, 2.2E−3, 6.4E−1); protein polymerization (0.92, 2.0E−3, 4.5E−1); cytoskeleton (0.92, 1.1E−2, 6.2E−1); 300 transit peptide (2, 8.4E−6, 7.7E−3); transit g g a g g c a a g peptide: Mitochondrion (2, 4.2E−4, 4.2E−1); A % 17.66 14.33 53 14.33 21 24 87 69.66 11.33 mitochondrion (2, 1.8E−3, 4.3E−1); ubiquinone (2, 3.2E−3, T % 12 4 5 6.666 6.666 2 0.333 4 2 5.2E−1); oxidoreductase (2, 3.3E−3, 4.5E−1); G % 51.66 70.33 21.66 63.66 40 32.33 11.33 19.33 63.33 intracellular membrane-bound organelle (1.75, 1.8E−3, C % 18.66 11.33 20.33 15.33 32.33 41.66 1.333 7 23.33 3.8E−1); membrane-bound organelle (1.75, 1.9E−3, 2.2E−1); cytoplasm (1.75, 3.5E−3, 2.7E−1); intracellular organelle (1.75, 6.8E−3, 3.6E−1); organelle (1.75, 7.2E−3, 3.2E−1); membrane (0.8, 7.3E−3, 6.8E−1); 289 glycoprotein (2.82, 3.1E−4, 2.5E−1); signal (2.82, 4.9E−4, g a a a g a a a c 2.0E−1); disulfide bond (2.82, 5.7E−4, 5.2E−1); A % 28.37 52.24 47.75 48.09 15.91 42.90 93.77 40.13 31.48 Propeptide, peptidase A1 (2.62, 1.6E−4, 3.8E−1); T % 13.14 14.87 13.14 22.49 24.91 5.882 0.692 7.266 5.882 Peptidase aspartic, active site (2.62, 4.5E−4, 4.8E−1); G % 44.29 12.11 21.45 22.83 40.48 9.342 4.152 28.71 22.14 pepsin A activity (2.62, 7.7E−4, 6.0E−1); aspartic-type C % 14.18 20.76 17.64 6.574 18.68 41.86 1.384 23.87 40.48 endopeptidase activity (2.62, 1.0E−3, 4.6E−1); duplication (1.45, 1.7E−3, 3.2E−1); cytokine (0.5, 7.9E−4, 2.2E−1); 223 signal (1.73, 5.3E−4, 3.9E−1); structural constituent of g c c g c c a g g ribosome (1.21, 4.7E−4, 4.3E−1); ribosome (1.21, 1.3E−3, A % 16.59 5.381 12.55 3.587 1.345 1.345 95.96 28.69 4.484 2.8E−1); ribonucleoprotein complex (1.21, 1.7E−3, 2.0E−1); T % 6.726 7.623 11.65 14.34 4.932 1.345 0.448 7.174 1.793 ribosomal protein (1.21, 1.9E−3, 5.9E−1); G % 56.50 23.76 4.035 56.05 33.63 4.484 2.242 62.33 57.39 C % 20.17 63.22 71.74 26.00 60.08 92.82 1.345 1.793 36.32 215 cellular physiological process (1.64, 2.2E−3, 6.6E−1); g c c g c c g c c cellular process (1.64, 2.4E−3, 5.9E−1); cellular protein A % 2.325 1.395 5.116 3.720 3.255 17.67 28.83 1.395 0.465 metabolism (1.64, 3.2E−3, 6.1E−1); coated vesicle T % 0.930 2.325 6.511 1.395 4.186 0 0 1.860 0.930 membrane (1.43, 7.3E−3, 3.8E−1); vesicle coat (1.43, G % 90.69 2.790 24.65 90.69 35.81 5.116 70.69 2.325 5.116 7.3E−3, 3.8E−1); membrane coat (1.43, 7.3E−3, 3.8E−1); C % 6.046 93.48 63.72 4.186 56.74 77.20 0.465 94.41 93.48 coated membrane (1.43, 7.3E−3, 3.8E−1); vesicle membrane (1.43, 8.8E−3, 3.2E−1); cytoplasmic vesicle membrane (1.43, 8.8E−3, 3.2E−1); coated vesicle (1.43, 1.2E−2, 3.7E−1); cytoplasmic membrane-bound vesicle (1.43, 2.2E−2, 5.1E−1); vesicle (1.43, 2.2E−2, 5.1E−1); membrane-bound vesicle (1.43, 2.2E−2, 5.1E−1); cytoplasmic vesicle (1.43, 2.2E−2, 5.1E−1); cytoskeleton (1.35, 3.6E−2, 5.0E−1); microtubule cytoskeleton (1.35, 4.9E−2, 5.8E−1); cellular physiological process (0.95, 2.2E−3, 6.6E−1); cellular process (0.95, 2.4E−3, 5.9E−1); intracellular non-membrane-bound organelle (0.83, 3.4E−2, 5.3E−1); non-membrane-bound organelle (0.83, 3.4E−2, 5.3E−1); 213 signal (2.88, 2.8E−7, 2.6E−4); glycoprotein (2.88, 2.1E−5, 9.6E−3); c a g g a c a g c membrane (2.88, 1.1E−4, 3.3E−2); A % 18.30 39.43 18.77 40.84 41.31 8.450 94.36 23.94 10.79 T % 11.73 15.49 6.103 6.572 29.57 0.469 0.469 27.69 2.816 G % 19.24 28.63 70.42 43.19 25.82 35.21 0.469 32.39 32.86 C % 50.70 16.43 4.694 9.389 3.286 55.86 4.694 15.96 53.52 84 nucleotide-binding (1.14, 2.2E−4, 1.8E−1); a c c a g c a c c atp-binding (1.14, 1.2E−3, 4.2E−1); A % 66.66 2.380 5.952 78.57 3.571 1.190 92.85 4.761 8.333 T % 10.71 3.571 1.190 14.28 1.190 0 0 5.952 3.571 G % 3.571 44.04 19.04 4.761 84.52 3.571 3.571 19.04 2.380 C % 19.04 50 73.80 2.380 10.71 95.23 3.571 70.23 85.71 66 membrane-bound organelle (1.11, 7.9E−3, 6.5E−1); g g a g g g a a g A % 21.21 13.63 68.18 6.060 0 9.090 95.45 93.93 3.030 T % 15.15 3.030 9.090 0 7.575 0 0 0 1.515 G % 33.33 43.93 22.72 86.36 89.39 46.96 4.545 1.515 90.90 C % 30.30 39.39 0 7.575 3.030 43.93 0 4.545 4.545 52 nad (1.73, 1.2E−3, 6.7E−1); g t c c c a g c c A % 3.846 25 21.15 1.923 5.769 38.46 0 19.23 5.769 T % 7.692 40.38 7.692 5.769 0 23.07 0 0 0 G % 67.30 28.84 34.61 0 0 28.84 92.30 11.53 0 C % 21.15 5.769 36.53 92.30 94.23 9.615 7.692 69.23 94.23 33 ribonucleoprotein complex (1.11, 1.2E−2, 6.7E−1); g c c g c c a g c A % 15.15 6.060 21.21 0 0 0 93.93 6.060 12.12 T % 0 0 15.15 0 3.030 0 0 39.39 6.060 G % 78.78 0 0 100 0 0 0 54.54 0 C % 6.060 93.93 63.63 0 96.96 100 6.060 0 81.81 32 Cathelicidin (3.99, 8.6E−10, 2.5E−6); antibiotic (3.99, 2.6E−7, 2.4E−4); c g g g g g g c c antimicrobial (3.99, 4.6E−7, 2.1E−4); pyrrolidone carboxylic acid A % 0 3.125 0 3.125 6.25 15.62 40.62 6.25 0 (3.99, 1.1E−6, 3.4E−4); T % 0 31.25 6.25 0 0 3.125 0 0 3.125 G % 0 40.62 56.25 96.87 93.75 81.25 59.37 0 3.125 C % 100 25 37.5 0 0 0 0 93.75 93.75 30 Lipid-binding serum glycoprotein (0.94, 2.5E−4, 5.2E−1); c a g g a gc a a g A % 20 40 0 30 86.66 0 100 96.66 0 T % 3.333 30 0 3.333 10 0 0 0 0 G % 0 23.33 96.66 66.66 0 50 0 3.333 100 C % 76.66 6.666 3.333 0 3.333 50 0 0 0 29 Cathelicidin (5.17, 1.6E−11, 4.6E−8); pyrrolidone carboxylic acid (5.17, c t c g g c a c c 3.5E−9, 3.2E−6); antibiotic (5.17, 3.8E−8, 1.8E−5); antimicrobial A % 6.896 0 0 6.896 3.448 24.13 96.55 3.448 3.448 (5.17, 7.9E−8, 2.4E−5); SF001637: cathelin (5.17, 2.3E−4, 2.1E−1); T % 20.68 96.55 13.79 0 0 0 0 0 0 signal (5.17, 4.9E−4, 1.1E−1); G % 3.448 3.448 41.37 93.10 93.10 24.13 0 3.448 0 C % 68.96 0 44.82 0 3.448 51.72 3.448 93.10 96.55

REFERENCES

-   ¹Everitt. B., Cluster Analysis, Edward Arnold, London, 1993 -   ¹W. Thong, G. Altun, R. Harrison, P.C. Tai, and Y. Pan, Improved     K-Means Clustering Algorithm for Exploring Local Protein Sequence     Motifs, Representing. Common Structural Property, IEEE TRANSACTIONS     ON NANOBIOSCIENCE, VOL. 4, NO. 3, SEPTEMBER 2005. -   ¹K. F. Han, D. Baker, Recurring local sequence motifs in proteins J.     Mol. Biol., vol. 251(1), pages 176-187, 1995 -   ¹Heidecker G, Messing J: Structural analysis of plant genes. Annu.     Rev. Plant Physiol. 37, 439-466 (1986) -   ¹C. P. Joshi, An Inspection of the domain putative An inspection of     the domain between putative TATA box and translation start site in     79 plant genes, Nucleic Acids Research, 1987, Vol. 15, No. 16     6643-6653. -   ¹C. P. Joshi, H. Thou, X. Huang and V. L. Chiang, Context sequences     of translation initiation codon in plants, Plant Molecular Biology     35: 993-1001, 1997; Q. Liu, Q. Xue, Comparative studies on sequence     characteristics around translation initiation codon in four     eukaryotes, Journal of Genetics, Vol. 84, No. 3, December 2005. -   ¹M. Jaiswal, L. Rangan, Context Sequence For Transcription Factors     Surrounding Start Codon in Model Crops, CURRENT SCIENCE, VOL. 93,     NO. 2, 25 JUL. 2007. -   ¹Kozak M. Nucleotide sequences of 5′-terminal ribosome-protected     initiation regions from two reovirus messages. Nature. 1977 Sep. 29;     269(5627):391-4; Kozak M. Possible role of flanking nucleotides in     recognition of the AUG initiator codon by eukaryotic ribosomes.     Nucleic Acids Res. 1981 Oct. 24; 9(20):5233-52.; Kozak M. Sequences     of ribosome binding sites from the large size class of reovirus     mRNA. J. Virol. 1982 May; 42(2):467-73 -   ¹Compilation and analysis of sequences upstream from the     translational start site in eukaryotic mRNAs. Nucleic Acids Res.     1984 Jan. 25;12(2):857-72.;Kozak M: An analysis of 50-noncoding     sequences from 699 vertebrate messenger RNAs. Nucl Acids Res 15,     8125-8148 (1987); Kozak M: At least six nucleotides preceding the     AUG initiator codon enhance translation in mammalian cells. J Mol     Biol 196: 947-950 (1987). -   ¹Samir V. S., Pradhyumna K. S., Shiv K. G., Raju M. and Rakesh T,     Conserved nucleotide sequences in highly expressed genes in plants,     Journal of Genetics, Vol. 78, No. 2, August 1999 123. -   ¹Taylor J L, Jones J D G, Sandler S, Mueller G M, Bedbrook J,     Dunsmuir, Optimizing the Expression of Chimeric Genes in Plant     Cells, Mol. Gen. Genet. (1987)210, pages 572-577. -   ¹Sleat D. E., Gallie D. R, Jefferson R. A., Bevan M. W., Turner P.     C., Wilson T. M. A., Characterization of the 50-leader Sequence of     Tobacco Mosaic Virus RNA as a General Enhancer of Translation in     vitro, Gene (1987)217: 217-225. -   ¹Chandrashekhar P. Joshi, Hao Zhou, Xiaoqiu Huang and Vincent L.     Chiang, Context sequences of translation initiation codon in plants,     Plant Molecular Biology 35: 993-1001, 1997, at p. 998 below. -   ¹C. P. Joshi, H. Zhou, X. Huang and V. L. Chiang, Context sequences     of translation initiation codon in plants, Plant Molecular Biology     (1997)35: 993-1001, see Table 3 at p. 1000. -   ¹See for example U.S. Pat. No. 7,253,342. -   ¹D. Arthur, S. Vassilvitskii, How Slow is the k Means Method?, 2006     (Stanford , yet unpublished). See     http://www.stanford.edu/˜sergeiv/papers/kMeans-socg.pdf -   ¹Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest (1990):     Introduction to algorithms. MIT Press/McGraw-Hill. -   ¹Dennis G Jr, Sherman B T, Hosack D A, Yang J, Gao W, Lane H C,     Lempicki R A. DAVID: Database for Annotation, Visualization, and     Integrated Discovery. Genome Biology 2003, 4(5). -   ¹D. A Hosack, G. D. Jr, B. T Sherman, H C. Lane, R. A Lempicki.     Identifying Biological Themes within Lists of Genes with EASE.     Genome Biology 2003 4(6). -   ¹Dennis G Jr, Sherman B T, Hosack D A, Yang J, Gao W, Lane H C,     Lempicki R A. DAVID: Database for Annotation, Visualization, and     Integrated Discovery. Genome Biology 2003, 4(5). -   ¹D. A Hosack, G. D. Jr, B. T Sherman, H C. Lane, R. A Lempicki.     Identifying Biological Themes within Lists of Genes with EASE.     Genome Biology 2003 4(6). -   ¹Chandrashekhar P. Joshi, Hao Zhou, Xiaoqiu Huang and Vincent L.     Chiang, Context sequences of translation initiation codon in plants,     Plant Molecular Biology 35: 993-1001, 1997. -   ¹Chandrashekhar P. Joshi, Hao Zhou, Xiaoqiu Huang and Vincent L.     Chiang, Context sequences of translation initiation codon in plants,     Plant Molecular Biology 35: 993-1001, 1997, at p. 999. 

1. A computer implemented method for obtaining a repository of attributes sets, wherein attributes sets are statistically associated with a sequence template representing two or more context sequences, comprising: (a) obtaining a dataset of context sequences; (b) transforming each context sequence to a sequence template, thereby obtaining a dataset of sequence templates; (c) clustering said dataset of sequence templates into a plurality of clusters according to a distance formula; wherein at least one cluster is statistically associated with at least one attributes set; and (d) inserting into said repository each of said clusters and said attributes set which is statistically associated with said each of said clusters.
 2. The computer implemented of claim 1, wherein said dataset of context sequences of step (a) is further subjected to multiple sequence alignment.
 3. A repository obtained by the computer implemented method of claim
 1. 4. A computer implemented method for identifying a sequence template as statistically associated with an attributes set of interest, comprising: (a) providing a repository of attributes sets; wherein attributes sets are statistically associated with a sequence template representing two or more context sequences; (b) selecting an attributes set; and (c) retrieving at least one sequence template statistically associated with said attributes set.
 5. The computer implemented method of claim 4, further comprising the step of merging at least two of said retrieved sequence templates.
 6. The computer implemented method of claim 4, said attributes are selected from the group consisting of the Gene Ontology Project (GO), Interpro annotation (European Molecular Biology Laboratory, EMBL), SMART (a Simple Modular Architecture Research Tool, found at http://smart.embl.de/), UniProt Knowledgebase (SwissProt), OMIM (by NCBI) PROSITE (by the Swiss Institute of Bioinformatics), Protein Information Resource (PIR), GeneCards, and Kyoto Encyclopedia of Genes and Genomes (KEGG).
 7. A method of preparing a polynucleotide construct, comprising: (a) identifying a sequence template as statistically associated with an attributes set of interest according to the method of claim 4; and (b) preparing a polynucleotide construct having at least one portion operably linked to a context sequence; wherein said context sequence is characterized as having either 80%-85%, 85%-90%, or 90%-100% homology with said sequence template.
 8. The method of claim 7, wherein the preparing comprises synthesizing said context sequence.
 9. The method of claim 7, wherein the preparing comprises constructing an expression vector comprising said context sequence.
 10. The method of claim 7, wherein the preparing comprises constructing a probe comprising said context sequence.
 11. A computer memory system comprising a plurality of tree topologies representing plurality of (k) heaps, wherein the plurality of tree topologies is managed through a common interface; and (k≧1).
 12. The computer memory system of claim 11, wherein said heaps are min heaps.
 13. The computer memory system of claim 11, wherein said heaps are max heaps.
 14. The computer memory system of claim 11, wherein an active subset of heaps is held in Random Access Memory (RAM), while the rest of said heaps are maintained on a secondary storage.
 15. A computer implemented method for clustering a plurality of polynucleotide sequences, comprising: (a) determining an attributes set for the plurality of polynucleotide sequences; and (b) clustering said polynucleotide sequences into a plurality of clusters according to values of said attributes set.
 16. A computerized system configured for identifying a sequence template as statistically associated with an attributes set of interest, the computerized system comprising: context sequence clustering module, configured to cluster said sequences into a plurality of clusters; and an enrichment analysis module, configured to provide enrichment appraisal, wherein context sequence clustering module being communicatively coupled to the enrichment analysis module. 